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Dissertations / Theses on the topic 'Incremental learning'
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Westendorp, James Computer Science & Engineering Faculty of Engineering UNSW. "Robust incremental relational learning." Awarded by:University of New South Wales. Computer Science & Engineering, 2009. http://handle.unsw.edu.au/1959.4/43513.
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Real-world learning tasks present a range of issues for learning systems. Learning tasks can be complex and the training data noisy. When operating as part of a larger system, there may be limitations on available memory and computational resources. Learners may also be required to provide results from a stream. This thesis investigates the problem of incremental, relational learning from imperfect data with constrained time and memory resources. The learning process involves incremental update of a theory when an example is presented that contradicts the theory. Contradictions occur if there is an incorrect theory or noisy data. The learner cannot discriminate between the two possibilities, so both are considered and the better possibility used. Additionally, all changes to the theory must have support from multiple examples. These two principles allow learning from imperfect data. The Minimum Description Length principle is used for selection between possible worlds and determining appropriate levels of additional justification. A new encoding scheme allows the use of MDL within the framework of Inductive Logic Programming. Examples must be stored to provide additional justification for revisions without violating resource requirements. A new algorithm determines when to discard examples, minimising total usage while ensuring sufficient storage for justifications. Searching for revisions is the most computationally expensive part of the process, yet not all searches are successful. Another new algorithm uses a notion of theory stability as a guide to occasionally disallow entire searches to reduce overall time. The approach has been implemented as a learner called NILE. Empirical tests include two challenging domains where this type of learner acts as one component of a larger task. The first of these involves recognition of behavior activation conditions in another agent as part of an opponent modeling task. The second, more challenging task is learning to identify objects in visual images by recognising relationships between image features. These experiments highlight NILE'S strengths and limitations as well as providing new n domains for future work in ILP.
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HILLNERTZ, FREDRIK. "Incremental Self Learning Road map." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155910.
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This paper describes a system that incrementally constructs an increasingly accurate road map from GPS traces from a single vehicle. The resulting road map contains information about the road such as road gradient which can be used by functions in a heavy vehicle to drive more effectively. The system is supposed to run on an embedded system in a heavy vehicle and is therefore design to require as little working memory and processing time as possible.Pre- and post processing techniques that counters GPS noise, random movements and improve the quality of the road map are also described, for example tunnel estimation where GPS signals are missing. An aging method, designed for data from a single vehicle, that eventually removes closed and rarely used roads is proposed.A comparison between the constructed road map and a commercial one shows that the algorithms described creates a very accurate roadmap. The performance of the system is evaluated and it is concluded that it would be possible to run it on an embedded system in a heavyvehicle.
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Kim, Min Sub Computer Science & Engineering Faculty of Engineering UNSW. "Reinforcement learning by incremental patching." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/39716.
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This thesis investigates how an autonomous reinforcement learning agent can improve on an approximate solution by augmenting it with a small patch, which overrides the approximate solution at certain states of the problem. In reinforcement learning, many approximate solutions are smaller and easier to produce than ???flat??? solutions that maintain distinct parameters for each fully enumerated state, but the best solution within the constraints of the approximation may fall well short of global optimality. This thesis proposes that the remaining gap to global optimality can be efficiently minimised by learning a small patch over the approximate solution. In order to improve the agent???s behaviour, algorithms are presented for learning the overriding patch. The patch is grown around particular regions of the problem where the approximate solution is found to be deficient. Two heuristic strategies are proposed for concentrating resources to those areas where inaccuracies in the approximate solution are most costly, drawing a compromise between solution quality and storage requirements. Patching also handles problems with continuous state variables, by two alternative methods: Kuhn triangulation over a fixed discretisation and nearest neighbour interpolation with a variable discretisation. As well as improving the agent???s behaviour, patching is also applied to the agent???s model of the environment. Inaccuracies in the agent???s model of the world are detected by statistical testing, using a selective sampling strategy to limit storage requirements for collecting data. The patching algorithms are demonstrated in several problem domains, illustrating the effectiveness of patching under a wide range of conditions. A scenario drawn from a real-time strategy game demonstrates the ability of patching to handle large complex tasks. These contributions combine to form a general framework for patching over approximate solutions in reinforcement learning. Complex problems cannot be solved by brute force alone, and some form of approximation is necessary to handle large problems. However, this does not mean that the limitations of approximate solutions must be accepted without question. Patching demonstrates one way in which an agent can leverage approximation techniques without losing the ability to handle fine yet important details.
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Giritharan, Balathasan. "Incremental Learning with Large Datasets." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149595/.
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This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation.
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Monica, Riccardo. "Deep Incremental Learning for Object Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12331/.
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In recent years, deep learning techniques received great attention in the field of information technology. These techniques proved to be particularly useful and effective in domains like natural language processing, speech recognition and computer vision. In several real world applications deep learning approaches improved the state-of-the-art. In the field of machine learning, deep learning was a real revolution and a number of effective techniques have been proposed for both supervised and unsupervised learning and for representation learning. This thesis focuses on deep learning for object recognition, and in particular, it addresses incremental learning techniques. With incremental learning we denote approaches able to create an initial model from a small training
set and to improve the model as new data are available. Using temporal coherent sequences proved to be useful for incremental learning since temporal coherence also allows to operate in unsupervised manners. A critical point of incremental learning is called forgetting which is the risk to forget previously learned patterns as new data are presented. In the first chapters of this work we introduce the basic theory on neural networks, Convolutional Neural Networks and incremental learning. CNN is today one of the most effective approaches for supervised object recognition; it is well accepted by the scientific community and largely used by ICT big players like Google and Facebook:
relevant applications are Facebook face recognition and Google image search. The scientific community has several (large) datasets (e.g., ImageNet) for the development and evaluation of object recognition approaches. However very few temporally coherent datasets are available to study incremental approaches. For this reason we decided to collect a new dataset named TCD4R (Temporal Coherent Dataset For Robotics).
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Sindhu, Muddassar. "Incremental Learning and Testing of Reactive Systems." Licentiate thesis, KTH, Teoretisk datalogi, TCS, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-37763.
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This thesis concerns the design, implementation and evaluation of a specification based testing architecture for reactive systems using the paradigm of learning-based testing. As part of this work we have designed, verified and implemented new incremental learning algorithms for DFA and Kripke structures.These have been integrated with the NuSMV model checker to give a new learning-based testing architecture. We have evaluated our architecture on case studies and shown that the method is effective.QC 20110822
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Suryanto, Hendra Computer Science & Engineering Faculty of Engineering UNSW. "Learning and discovery in incremental knowledge acquisition." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2005. http://handle.unsw.edu.au/1959.4/20744.
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Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.
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Florez-Larrahondo, German. "Incremental learning of discrete hidden Markov models." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-05312005-141645.
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MOTTA, EDUARDO NEVES. "SUPERVISED LEARNING INCREMENTAL FEATURE INDUCTION AND SELECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28688@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
A indução de atributos não lineares a partir de atributos básicos é um modo de obter modelos preditivos mais precisos para problemas de classificação. Entretanto, a indução pode causar o rápido crescimento do número de atributos, resultando usualmente em overfitting e em modelos com baixo poder de generalização. Para evitar esta consequência indesejada, técnicas de regularização são aplicadas, para criar um compromisso entre um reduzido conjunto de atributos representativo do domínio e a capacidade de generalização Neste trabalho, descrevemos uma abordagem de aprendizado de máquina supervisionado com indução e seleção incrementais de atributos. Esta abordagem integra árvores de decisão, support vector machines e seleção de atributos utilizando perceptrons esparsos em um framework de aprendizado que chamamos IFIS – Incremental Feature Induction and Selection. Usando o IFIS, somos capazes de criar modelos regularizados não lineares de alto desempenho utilizando um algoritmo com modelo linear. Avaliamos o nosso sistema em duas tarefas de processamento de linguagem natural em dois idiomas. Na primeira tarefa, anotação morfossintática, usamos dois corpora, o corpus WSJ em língua inglesa e o Mac-Morpho em Português. Em ambos, alcançamos resultados competitivos com o estado da arte reportado na literatura, alcançando as acurácias de 97,14 por cento e 97,13 por cento, respectivamente. Na segunda tarefa, análise de dependência, utilizamos o corpus da CoNLL 2006 Shared Task em português, ultrapassando os resultados reportados durante aquela competição e alcançando resultados competitivos com o estado da arte para esta tarefa, com a métrica UAS igual a 92,01 por cento. Com a regularização usando um perceptron esparso, geramos modelos SVM que são até 10 vezes menores, preservando sua acurácia. A redução dos modelos é obtida através da regularização dos domínios dos atributos, que atinge percentuais de até 99 por cento. Com a regularização dos modelos, alcançamos uma redução de até 82 por cento no tamanho físico dos modelos. O tempo de predição do modelo compacto é reduzido em até 84 por cento. A redução dos domínios e modelos permite também melhorar a engenharia de atributos, através da análise dos domínios compactos e da introdução incremental de novos atributos.
Non linear feature induction from basic features is a method of generating predictive models with higher precision for classification problems. However, feature induction may rapidly lead to a huge number of features, causing overfitting and models with low predictive power. To prevent this side effect, regularization techniques are employed to obtain a trade-off between a reduced feature set representative of the domain and generalization power. In this work, we describe a supervised machine learning approach that incrementally inducts and selects feature conjunctions derived from base features. This approach integrates decision trees, support vector machines and feature selection using sparse perceptrons in a machine learning framework named IFIS – Incremental Feature Induction and Selection. Using IFIS, we generate regularized non-linear models with high performance using a linear algorithm. We evaluate our system in two natural language processing tasks in two different languages. For the first task, POS tagging, we use two corpora, WSJ corpus for English, and Mac-Morpho for Portuguese. Our results are competitive with the state-of-the-art performance in both, achieving accuracies of 97.14 per cent and 97.13 per cent, respectively. In the second task, Dependency Parsing, we use the CoNLL 2006 Shared Task Portuguese corpus, achieving better results than those reported during that competition and competitive with the state-of-the-art for this task, with UAS score of 92.01 per cent. Applying model regularization using a sparse perceptron, we obtain SVM models 10 times smaller, while maintaining their accuracies. We achieve model reduction by regularization of feature domains, which can reach 99 per cent. Using the regularized model we achieve model physical size shrinking of up to 82 per cent. The prediction time is cut by up to 84 per cent. Domains and models downsizing also allows enhancing feature engineering, through compact domain analysis and incremental inclusion of new features.
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Tortajada, Velert Salvador. "Incremental Learning approaches to Biomedical decision problems." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17195.
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During the last decade, a new trend in medicine is transforming the nature of healthcare from reactive to proactive. This new paradigm is changing into a personalized medicine where the prevention, diagnosis, and treatment of disease is focused on individual patients. This paradigm is known as P4 medicine. Among other key benefits, P4 medicine aspires to detect diseases at an early stage and introduce diagnosis to stratify patients and diseases to select the optimal therapy based on individual observations and taking into account the patient outcomes to empower the physician, the patient, and their communication.
This paradigm transformation relies on the availability of complex multi-level biomedical data that are increasingly accurate, since it is possible to find exactly the needed information, but also exponentially noisy, since the access to that information is more and more challenging. In order to take advantage of this information, an important effort is being made in the last decades to digitalize medical records and to develop new mathematical and computational methods for extracting maximum knowledge from patient records, building dynamic and disease-predictive models from massive amounts of integrated clinical and biomedical data. This requirement enables the use of computer-assisted Clinical Decision Support Systems for the management of individual patients.
The Clinical Decision Support System (CDSS) are computational systems that provide precise and specific knowledge for the medical decisions to be adopted for diagnosis, prognosis, treatment and management of patients. The CDSS are highly related to the concept of evidence-based medicine since they infer medical knowledge from the biomedical databases and the acquisition protocols that are used for the development of the systems, give computational support based on evidence for the clinical practice, and evaluate the performance and the added value of the solution for each specific medical problem.Tortajada Velert, S. (2012). Incremental Learning approaches to Biomedical decision problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17195
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Montes, De Oca Roldan Marco. "Incremental social learning in swarm intelligence systems." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209909.
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A swarm intelligence system is a type of multiagent system with the following distinctive characteristics: (i) it is composed of a large number of agents, (ii) the agents that comprise the system are simple with respect to the complexity of the task the system is required to perform, (iii) its control relies on principles of decentralization and self-organization, and (iv) its constituent agents interact locally with one another and with their environment. Interactions among agents, either direct or indirect through the environment in which they act, are fundamental for swarm intelligence to exist; however, there is a class of interactions, referred to as "interference", that actually blocks or hinders the agents' goal-seeking behavior. For example, competition for space may reduce the mobility of robots in a swarm robotics system, or misleading information may spread through the system in a particle swarm optimization algorithm. One of the most visible effects of interference in a swarm intelligence system is the reduction of its efficiency. In other words, interference increases the time required by the system to reach a desired state. Thus, interference is a fundamental problem which negatively affects the viability of the swarm intelligence approach for solving important, practical problems.
We propose a framework called "incremental social learning" (ISL) as a solution to the aforementioned problem. It consists of two elements: (i) a growing population of agents, and (ii) a social learning mechanism. Initially, a system under the control of ISL consists of a small population of agents. These agents interact with one another and with their environment for some time before new agents are added to the system according to a predefined schedule. When a new agent is about to be added, it learns socially from a subset of the agents that have been part of the system for some time, and that, as a consequence, may have gathered useful information. The implementation of the social learning mechanism is application-dependent, but the goal is to transfer knowledge from a set of experienced agents that are already in the environment to the newly added agent. The process continues until one of the following criteria is met: (i) the maximum number of agents is reached, (ii) the assigned task is finished, or (iii) the system performs as desired. Starting with a small number of agents reduces interference because it reduces the number of interactions within the system, and thus, fast progress toward the desired state may be achieved. By learning socially, newly added agents acquire knowledge about their environment without incurring the costs of acquiring that knowledge individually. As a result, ISL can make a swarm intelligence system reach a desired state more rapidly.
We have successfully applied ISL to two very different swarm intelligence systems. We applied ISL to particle swarm optimization algorithms. The results of this study demonstrate that ISL substantially improves the performance of these kinds of algorithms. In fact, two of the resulting algorithms are competitive with state-of-the-art algorithms in the field. The second system to which we applied ISL exploits a collective decision-making mechanism based on an opinion formation model. This mechanism is also one of the original contributions presented in this dissertation. A swarm robotics system under the control of the proposed mechanism allows robots to choose from a set of two actions the action that is fastest to execute. In this case, when only a small proportion of the swarm is able to concurrently execute the alternative actions, ISL substantially improves the system's performance.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
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Lazarescu, Mihai M. "Incremental learning for querying multimodal symbolic data." Thesis, Curtin University, 2000. http://hdl.handle.net/20.500.11937/1660.
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In this thesis we present an incremental learning algorithm for learning and classifying the pattern of movement of multiple objects in a dynamic scene. The method that we describe is based on symbolic representations of the patterns. The typical representation has a spatial component that describes the relationships of the objects and a temporal component that describes the ordering of the actions of the objects in the scene. The incremental learning algorithm (ILF) uses evidence based forgetting, generates compact concept structures and can track concept drift.We also present two novel algorithms that combine incremental learning and image analysis. The first algorithm is used in an American Football application and shows how natural language parsing can be combined with image processing and expert background knowledge to address the difficult problem of classifying and learning American Football plays. We present in detail the model developed to representAmerican Football plays, the parser used to process the transcript of the American Football commentary and the algorithms developed to label the players and classify the queries. The second algorithm is used in a cricket application. It combines incremental machine learning and camera motion estimation to classify and learn common cricket shots. We describe the method used to extract and convert the camera motion parameter values to symbolic form and the processing involved in learning the shots.Finally, we explore the issues that arise from combining incremental learning with incremental recognition. Two methods that combine incremental recognition and incremental learning are presented along with a comparison between the algorithms.
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Lazarescu, Mihai M. "Incremental learning for querying multimodal symbolic data." Curtin University of Technology, School of Computing, 2000. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10010.
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In this thesis we present an incremental learning algorithm for learning and classifying the pattern of movement of multiple objects in a dynamic scene. The method that we describe is based on symbolic representations of the patterns. The typical representation has a spatial component that describes the relationships of the objects and a temporal component that describes the ordering of the actions of the objects in the scene. The incremental learning algorithm (ILF) uses evidence based forgetting, generates compact concept structures and can track concept drift.We also present two novel algorithms that combine incremental learning and image analysis. The first algorithm is used in an American Football application and shows how natural language parsing can be combined with image processing and expert background knowledge to address the difficult problem of classifying and learning American Football plays. We present in detail the model developed to representAmerican Football plays, the parser used to process the transcript of the American Football commentary and the algorithms developed to label the players and classify the queries. The second algorithm is used in a cricket application. It combines incremental machine learning and camera motion estimation to classify and learn common cricket shots. We describe the method used to extract and convert the camera motion parameter values to symbolic form and the processing involved in learning the shots.Finally, we explore the issues that arise from combining incremental learning with incremental recognition. Two methods that combine incremental recognition and incremental learning are presented along with a comparison between the algorithms.
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Chalup, Stephan Konrad. "Incremental learning with neural networks, evolutionary computation and reinforcement learning algorithms." Thesis, Queensland University of Technology, 2001.
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Kharma, Nawwaf Nayef. "An incremental machine learning mechanism for robotic applications." Thesis, Imperial College London, 1999. http://hdl.handle.net/10044/1/7957.
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Naidenova, Xenia, and Vladimir Parkhomenko. "An Approach to Incremental Learning Good Classification Tests." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113159.
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An algorithm of incremental mining implicative logical rules is pro-posed. This algorithm is based on constructing good classification tests. The in-cremental approach to constructing these rules allows revealing the interde-pendence between two fundamental components of human thinking: pattern recognition and knowledge acquisition.
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Sillito, Rowland R. "Incremental semi-supervised learning for anomalous trajectory detection." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4300.
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The acquisition of a scene-specific normal behaviour model underlies many existing approaches to the problem of automated video surveillance. Since it is unrealistic to acquire a comprehensive set of labelled behaviours for every surveyed scenario, modelling normal behaviour typically corresponds to modelling the distribution of a large collection of unlabelled examples. In general, however, it would be desirable to be able to filter an unlabelled dataset to remove potentially anomalous examples. This thesis proposes a simple semi-supervised learning framework that could allow a human operator to efficiently filter the examples used to construct a normal behaviour model by providing occasional feedback: Specifically, the classification output of the model under construction is used to filter the incoming sequence of unlabelled examples so that human approval is requested before incorporating any example classified as anomalous, while all other examples are automatically used for training. A key component of the proposed framework is an incremental one-class learning algorithm which can be trained on a sequence of normal examples while allowing new examples to be classified at any stage during training. The proposed algorithm represents an initial set of training examples with a kernel density estimate, before using merging operations to incrementally construct a Gaussian mixture model while minimising an information-theoretic cost function. This algorithm is shown to outperform an existing state-of-the-art approach without requiring off-line model selection. Throughout this thesis behaviours are considered in terms of whole motion trajectories: in order to apply the proposed algorithm, trajectories must be encoded with fixed length vectors. To determine an appropriate encoding strategy, an empirical comparison is conducted to determine the relative class-separability afforded by several different trajectory representations for a range of datasets. The results obtained suggest that the choice of representation makes a small but consistent difference to class separability, indicating that cubic B-Spline control points (fitted using least-squares regression) provide a good choice for use in subsequent experiments. The proposed semi-supervised learning framework is tested on three different real trajectory datasets. In all cases the rate of human intervention requests drops steadily, reaching a usefully low level of 1% in one case. A further experiment indicates that once a sufficient number of interventions has been provided, a high level of classification performance can be achieved even if subsequent requests are ignored. The automatic incorporation of unlabelled data is shown to improve classification performance in all cases, while a high level of classification performance is maintained even when unlabelled data containing a high proportion of anomalous examples is presented.
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Losing, Viktor [Verfasser]. "Memory Models for Incremental Learning Architectures / Viktor Losing." Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1191896420/34.
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Pinto, Rafael Coimbra. "Online incremental one-shot learning of temporal sequences." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49063.
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Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados.This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.
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Pinto, Rafael Coimbra. "Continuous reinforcement learning with incremental Gaussian mixture models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157591.
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A contribução original desta tese é um novo algoritmo que integra um aproximador de funções com alta eficiência amostral com aprendizagem por reforço em espaços de estados contínuos. A pesquisa completa inclui o desenvolvimento de um algoritmo online e incremental capaz de aprender por meio de uma única passada sobre os dados. Este algoritmo, chamado de Fast Incremental Gaussian Mixture Network (FIGMN) foi empregado como um aproximador de funções eficiente para o espaço de estados de tarefas contínuas de aprendizagem por reforço, que, combinado com Q-learning linear, resulta em performance competitiva. Então, este mesmo aproximador de funções foi empregado para modelar o espaço conjunto de estados e valores Q, todos em uma única FIGMN, resultando em um algoritmo conciso e com alta eficiência amostral, i.e., um algoritmo de aprendizagem por reforço capaz de aprender por meio de pouquíssimas interações com o ambiente. Um único episódio é suficiente para aprender as tarefas investigadas na maioria dos experimentos. Os resultados são analisados a fim de explicar as propriedades do algoritmo obtido, e é observado que o uso da FIGMN como aproximador de funções oferece algumas importantes vantagens para aprendizagem por reforço em relação a redes neurais convencionais.This thesis’ original contribution is a novel algorithm which integrates a data-efficient function approximator with reinforcement learning in continuous state spaces. The complete research includes the development of a scalable online and incremental algorithm capable of learning from a single pass through data. This algorithm, called Fast Incremental Gaussian Mixture Network (FIGMN), was employed as a sample-efficient function approximator for the state space of continuous reinforcement learning tasks, which, combined with linear Q-learning, results in competitive performance. Then, this same function approximator was employed to model the joint state and Q-values space, all in a single FIGMN, resulting in a concise and data-efficient algorithm, i.e., a reinforcement learning algorithm that learns from very few interactions with the environment. A single episode is enough to learn the investigated tasks in most trials. Results are analysed in order to explain the properties of the obtained algorithm, and it is observed that the use of the FIGMN function approximator brings some important advantages to reinforcement learning in relation to conventional neural networks.
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Hocquet, Guillaume. "Class Incremental Continual Learning in Deep Neural Networks." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST070.
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Nous nous intéressons au problème de l'apprentissage continu de réseaux de neurones artificiels dans le cas où les données ne sont accessibles que pour une seule catégorie à la fois. Pour remédier au problème de l'oubli catastrophique qui limite les performances d'apprentissage dans ces conditions, nous proposons une approche basée sur la représentation des données d'une catégorie par une loi normale. Les transformations associées à ces représentations sont effectuées à l'aide de réseaux inversibles, qui peuvent alors être entraînés avec les données d'une seule catégorie. Chaque catégorie se voit attribuer un réseau pour représenter ses caractéristiques. Prédire la catégorie revient alors à identifier le réseau le plus représentatif. L'avantage d'une telle approche est qu'une fois qu'un réseau est entraîné, il n'est plus nécessaire de le mettre à jour par la suite, chaque réseau étant indépendant des autres. C'est cette propriété particulièrement avantageuse qui démarque notre méthode des précédents travaux dans ce domaine. Nous appuyons notre démonstration sur des expériences réalisées sur divers jeux de données et montrons que notre approche fonctionne favorablement comparé à l'état de l'art. Dans un second temps, nous proposons d'optimiser notre approche en réduisant son impact en mémoire en factorisant les paramètres des réseaux. Il est alors possible de réduire significativement le coût de stockage de ces réseaux avec une perte de performances limitée. Enfin, nous étudions également des stratégies pour produire des réseaux capables d'être réutilisés sur le long terme et nous montrons leur pertinence par rapport aux réseaux traditionnellement utilisés pour l'apprentissage continuWe are interested in the problem of continual learning of artificial neural networks in the case where the data are available for only one class at a time. To address the problem of catastrophic forgetting that restrain the learning performances in these conditions, we propose an approach based on the representation of the data of a class by a normal distribution. The transformations associated with these representations are performed using invertible neural networks, which can be trained with the data of a single class. Each class is assigned a network that will model its features. In this setting, predicting the class of a sample corresponds to identifying the network that best fit the sample. The advantage of such an approach is that once a network is trained, it is no longer necessary to update it later, as each network is independent of the others. It is this particularly advantageous property that sets our method apart from previous work in this area. We support our demonstration with experiments performed on various datasets and show that our approach performs favorably compared to the state of the art. Subsequently, we propose to optimize our approach by reducing its impact on memory by factoring the network parameters. It is then possible to significantly reduce the storage cost of these networks with a limited performance loss. Finally, we also study strategies to produce efficient feature extractor models for continual learning and we show their relevance compared to the networks traditionally used for continual learning
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Yasin, Amanullah. "Incremental Bayesian network structure learning from data streams." Nantes, 2013. https://archive.bu.univ-nantes.fr/pollux/show/show?id=b81198e1-9d39-4282-9de6-f29ab95c0664.
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Dans la dernière décennie, l’extraction du flux de données est devenue un domaine de recherche très actif. Les principaux défis pour les algorithmes d’analyse de flux sont de gérer leur infinité, de s’adapter au caractère non stationnaire des distributions de probabilités sous-jacentes, et de fonctionner sans relecture. Par conséquent, les techniques traditionnelles de fouille ne peuvent s’appliquer directement aux flux de données. Le problème s’intensifie pour les flux dont les domaines sont de grande dimension tels que ceux provenant des réseaux sociaux, avec plusieurs centaines voire milliers de variables. Pour rester a jour, les algorithmes d’apprentissage de réseaux Bayésiens doivent pouvoir intégrer des données nouvelles en ligne. L’état de l’art en la matiere implique seulement plusieurs dizaines de variables et ces algorithmes ne fonctionnent pas correctement pour des dimensions supérieures. Ce travail est une contribution au problème d’apprentissage de structure de réseau Bayésien en ligne pour des domaines de haute dimension, et a donné lieu à plusieurs propositions. D’abord, nous avons proposé une approche incrémentale de recherche locale, appelée iMMPC. Ensuite, nous avons proposé une version incrémentale de l’algorithme MMHC pour apprendre la structure du réseau. Nous avons également adapté cet algorithme avec des mécanismes de fenêtre glissante et une pondération privilégiant les données nouvelles. Enfin, nous avons démontré la faisabilité de notre approche par de nombreuses expériences sur des jeux de données synthétiquesIn the last decade, data stream mining has become an active area of research, due to the importance of its applications and an increase in the generation of streaming data. The major challenges for data stream analysis are unboundedness, adaptiveness in nature and limitations over data access. Therefore, traditional data mining techniques cannot directly apply to the data stream. The problem aggravates for incoming data with high dimensional domains such as social networks, bioinformatics, telecommunication etc, having several hundreds and thousands of variables. It poses a serious challenge for existing Bayesian network structure learning algorithms. To keep abreast with the latest trends, learning algorithms need to incorporate novel data continuously. The existing state of the art in incremental structure learning involves only several tens of variables and they do not scale well beyond a few tens to hundreds of variables. This work investigates a Bayesian network structure learning problem in high dimensional domains. It makes a number of contributions in order to solve these problems. In the first step we proposed an incremental local search approach iMMPC to learn a local skeleton for each variable. Further, we proposed an incremental version of Max-Min Hill-Climbing (MMHC) algorithm to learn the whole structure of the network. We also proposed some guidelines to adapt it with sliding and damped window environments. Finally, experimental results and theoretical justifications that demonstrate the feasibility of our approach demonstrated through extensive experiments on synthetic datasets
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Furuhashi, Takeshi, Tomohiro Yoshikawa, and Takanori Yokoi. "Incremental learning to reduce the burden of machine learning for P300 speller." IEEE, 2012. http://hdl.handle.net/2237/20854.
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2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) (SCIS-ISIS 2012). November 20-24, 2012, Kobe, Japan
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Dhoble, Kshitij. "Incremental nonparametric discriminant analysis based active learning and its applications." AUT University, 2010. http://hdl.handle.net/10292/834.
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Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such biologically inspired model, designed to mimic humans’ dynamic, evolving, adaptive and intelligent cognitive learning ability. Active learning is a class of learning algorithms that aim to create an accurate classifier by iteratively selecting essentially important unlabeled data points by the means of adaptive querying and training the classifier on those data points which are potentially useful for the targeted learning task (Tong & Koller, 2002). The traditional active learning techniques are implemented under supervised or semi-supervised learning settings (Pang et al., 2009). Our proposed model performs the active learning in an unsupervised setting by introducing a discriminative selective sampling criterion, which reduces the computational cost by substantially decreasing the number of irrelevant instances to be learned by the classifier. The methods based on passive learning (which assumes the entire dataset for training is truly informative and is presented in advance) prove to be inadequate in a real world application (Pang et al., 2009). To overcome this limitation, we have developed Active Mode Incremental Nonparametric Discriminant Analysis (aIncNDA) which undertakes adaptive discriminant selection of the instances for an incremental NDA learning. NDA is a discriminant analysis method that has been incorporated in our selective sampling technique in order to reduce the effects of the outliers (which are anomalous observations/data points in a dataset). It works with significant efficiency on the anomalous datasets, thereby minimizing the computational cost (Raducanu & Vitri´a, 2008). NDA is one of the methods used in the proposed active learning model. This thesis presents the research on a discrimination-based active learning where NDA is extended for fast discrimination analysis and data sampling. In addition to NDA, a base classifier (such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN)) is applied to discover and merge the knowledge from the newly acquired data. The performance of our proposed method is evaluated against benchmark University of California, Irvine (UCI) datasets, face image, and object image category datasets. The assessment that was carried out on the UCI datasets showed that Active Mode Incremental NDA (aIncNDA) performs at par and in many cases better than the incremental NDA with a lower number of instances. Additionally, aIncNDA also performs efficiently under the different levels of redundancy, but has an improved discrimination performance more often than a passive incremental NDA. In an application that undertakes the face image and object image recognition and retrieval task, it can be seen that the proposed multi-example active learning system dynamically and incrementally learns from the newly obtained images, thereby gradually reducing its retrieval (classification) error rate by the means of iterative refinement. The results of the empirical investigation show that our proposed active learning model can be used for classification with increased efficiency. Furthermore, given the nature of network data which is large, streaming, and constantly changing, we believe that our method can find practical application in the field of Internet security.
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Ribes, Sanz Arturo. "Incremental active learning of sensorimotor models in developmental robotics." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/288046.
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La rápida evolución de la robótica esta promoviendo que emerjan nuevos campos relacionados con la robótica. Inspirándose en ideas provinientes de la psicología del desarrollo, la robótica del desarrollo es un nuevo campo que pretende proveer a los robots de capacidades que les permiten aprender de una manera abierta durante toda su vida.
Hay situaciones donde los ingenieros o los diseñadores no pueden prever todos los posibles problemas que un robot pueda encontrar. Tal como el número de tareas que un robot debe hacer crece, este problema se vuelve más evidente, y las soluciones de ingenería tradicionales pueden no ser completamente factibles.
En tal caso, la robótica del desarrollo proporciona una serie de principios y directrices para construir robots que tienen las herramientas cognitivas adecuadas a fin de adquirir el conocimiento necesario.
Auto-exploración, aprendizaje incremental, scaffolding social e imitación. Todas son herramientas que contribuyen a construir robots con un alto grado de autonomía. Mediante la auto-exploración internamente motivada, un robot descubre lo que su cuerpo es capaz de hacer. Las técnicas de aprendizaje incremental permite que un robot tenga conocimiento listo al instante, a partir de construir estructuras cognitivas encima de otras más viejas. El scaffolding o andamiaje social y las capacidades de imitación permiten aprovechar lo que los humanos --- u otros robots --- ya saben. De esta manera, los robots tienen metas que perseguir y aportan, o bien un uso final para las habilidades aprendidas, o bien ejemplos de cómo lograr una determinada tarea.
Esta tesis presenta un estudio de una serie de técnicas, las cuales ejemplifican cómo algunos de esos principios, aplicados a robots reales, funcionan juntos, permitiendo al robot aprender autónomamente a ejecutar una serie de tareas. También mostramos cómo el robot, aprovechándose de técnicas de aprendizaje activo e incremental, es capaz de decidir la mejor manera de explorar su entorno a fin de adquirir el conocimiento que mejor le ayuda a lograr sus objetivos. Ésto, añadido al descubrimiento autónomo de las limitaciones de su propio cuerpo, disminuye la cantidad de conocimiento especifico del dominio que es necesario poner en el diseño del sistema de aprendizaje.
Primeramente, presentamos un algoritmo de aprendizaje incremental para Modelos de Mixtura de Gaussianas aplicado al problema de aprendizaje sensorimotor. Implementado en un robot móvil, el objetivo es adquirir un modelo que es capaz de realizar predicciones sobre los estados sensoriales futuros. Este modelo predictivo es reutilizado como substrato representacional, el cual sirve para categorizar y anticipar situaciones tales como la colisión contra un objeto.
Después de un periodo extendido de aprendizaje, y habiendo encontrado situaciones diferentes, observamos que los modelos adquiridos se terminan siendo bastante grandes. Sin embargo, nos dimos cuenta que, en un momento dado, solo una pequeña porcion del mismo es utilizada. Además, estas areas son utilizadas consistentemente por un periodo relativamente largo de tiempo. Presentamos una extensión para el algoritmo de Regresión basado en Mixturas de Gaussianas, el cual aprovecha este hecho a fin de reducir el coste computacional de la inferencia.
Las técnicas aquí presentadas fueron también aplicadas en un problema diferente y más commplejo: la imitación de una secuencia de notas musicales proporcionadas por un humano. Estas son producidas por un objeto musical virtual que es utilizado por un robot humanoide. El robot no solo aprende a utilizar este objeto, sino que también aprende sobre las limitaciones de su propio cuerpo. Ésto le permite entender mejor qué puede hacer y cómo puede hacerlo, subrayando la importancia de la influencia que el hecho de tener cuerpo tiene en la interacción del robot con su entorno y el tipo de estructuras cognitivas que se forman como consecuencia de este tipo de interacción.The rapid evolution of robotics is promoting new robotics related research fields to emerge. Taking insights from developmental psychology, developmental robotics is a new field which aims to endow robots with capabilities that enable them to life-long learning in an open-ended way. There are situations where engineers or designers cannot foresee all the possible problems a robot may encounter. As the number of tasks that a robot must do grows, this problem becomes more evident and traditional engineering solutions may not be entirely feasible. In that case, developmental robotics provides a series of principles and guidelines to construct robots which have the adequate cognitive tools in order to acquire the necessary knowledge. Self-exploration, incremental learning, social scaffolding or imitation. All are tools which contribute to build robots with a high degree of autonomy. By means of internally motivated self-exploration, a robot discovers what its body is able to do. Incremental learning techniques enable a robot to have ready-to-use knowledge by building new cognitive structures on top of old ones. Social scaffolding and imitation capabilities allows taking advantage of what humans --- or other robots --- already know. In this way, robots have goals to pursue and provide either an end use of learned skills or examples on how to accomplish a given task. This thesis presents a study of a series of techniques which exemplify how some of those principles, applied to real robots, work together, enabling the robot to autonomously learn to perform a series of tasks. We also show how the robot, by taking advantage of active and incremental learning, is able to decide the best way to explore its environment in order to acquire knowledge that best helps in accomplishing its goals. This, in addition to the autonomous discovery of its own body limitations, leverages the amount of domain specific knowledge that needs to be put in the design of the learning system. First and foremost, we present an incremental learning algorithm for Gaussian Mixture Models applied to the problem of sensorimotor learning. Implemented in a mobile robot, the objective is to acquire a model which is capable of making predictions about future sensory states. This predictive model is reused as a representation substrate which serves to categorize and anticipate situations such as the collision with an object. After an extended period of learning, and having encountered different situations, we observed that the acquired models become quite large. However, we realized that, at any given time, only small portions of it are used. Furthermore, these areas are consistently used over relatively long periods of time. We present an extension to the standard Gaussian Mixture Regression algorithm which takes advantage of this fact in order to reduce the computational cost of inference. The techniques herein presented were also applied in a different and more complex problem: the imitation of a sequence of musical notes provided by a human. Those are produced by a virtual musical object which is used by a humanoid robot. The robot not only learns to use this object, but also learns about its own body limitations. This enables it to better understand what it is able to do and how, highlighting the importance of embodiment in the interaction of a robot with its environment and the kind of cognitive structures that are formed as a consequence of this type of interaction.
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Kapp, Marcelo Nepomoceno. "Dynamic optimization of classification systems for adaptive incremental learning." Mémoire, École de technologie supérieure, 2010. http://espace.etsmtl.ca/270/1/KAPP_Marcelo_Nepomoceno.pdf.
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Lors de I'arrivée de nouvelles données, un systeme d'apprentissage incremental se met a jour
automatiquement sans reexaminer les anciennes donnees. Lors d'un apprentissage incremental,
les parametres des systemes de classification ne sont plus consideres comme invariants
puisqu'ils peuvent evolucr en fonction des donnees entrantes. Ces changemcnts causent dcs
variations dans I'ajustement des parametres du systeme de classification. Si ces variations sont
negligees, la performance finale d'un tel systeme pent etre ulterieurement compromise. De tcls
systemes, adaptes au probleme de classification, sont tres utiles a des fins industrielles ou militaires
car ceux-ci sont a la fois rapides d'execution et peu gourmands en memoire. On observe
en consequence un interet grandissant a I'elaboration de tels systemes.
L'objectif principal de cette these est de developper un systeme capable de s'adapter de fa^on
incrementale a I'arrivee de nouvelles donnees, de suivrc et d'analyscr dynamiqucment les
parametres du systeme optimal pour ainsi pcrmcttrc son adaptation automatique a de nouvelles
situations. Pour ce faire, nous commen9ons par aborder le probleme de la selection optimale
des classificateurs en fonction du temps. Nous proposons une architecture qui combine la
puissance de la theorie de I'intelligence des essaims avec la methode plus conventionnelle de
recherche par grilles.
Des solutions potentielles sont progressivement identifices et mises en evidence pour des bases
de donnees graduellement mises a jour. L'idee principale ici est de considerer I'ajustement
des parametres du classificateur comme un probleme d'optimisation dynamique dependant des
donnees presentees au systeme de maniere continue. En particulier, nous avons montre que si
I'on cherchait a elaborer un classificateur SVM (Support Vector Machines) efficace a partir de
sources de donnees differentes, graduelles ou en series, mieux valait considerer le processus de
selection de modeles comme un processus dynamique qui pent evoluer et changer Ainsi, les
differentes solutions sont adaptees au fil du temps en fonction revolution des connaissances
accessibles sur le probleme de classifications et de I'incertitude sur les donnees.
Ensuite, nous etudions aussi des mesures pour revaluation et la selection d'ensembles de classificateurs
composes de SVMs. Les mesures employees sont basees sur les theories de la
diversite et la marge communement utilisees pour expliquer la performance des ensembles de
classificateurs. Cette etude revele des informations precieuses pour I'elaboration de mesures
de confiance pouvant servir pour la selection des ensembles de classificateurs.
Finalement, la contribution majeure de cette these est une approche d'optimisation dynamique
qui realise un apprentissage incremental et adaptatif en suivant, faisant evoluer et corabinant
V
les hypotheses d'optima en fonction du temps. L'approche fait usage de concepts issus de differentes
theories experimentales, telles quti I'optiraisation dynamique de particules d'essaims,
les classificateurs SVM incrementaux, la detection de changement et la selection dynamique
d'ensembles a partir de niveaux de confiance des classificateurs. Des experiences menees sur
des bases de donnees synthetiques et reelles montrent que I'approche proposee surpasse les
autres methodes de classification souvent utilisees dans des scenarios d'apprentissage incremental.
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Tschorn, Patrick. "Incremental inductive logic programming for learning from annotated copora." Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538607.
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Kapp, Marcelo Nepomoceno. "Dynamic optimization of classification systems for adaptive incremental learning." reponame:Repositório Institucional da UNILA, 2016. http://dspace.unila.edu.br/123456789/550.
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Tese de Doutorado, defendida na Université Du Québec, Canadian. 2010Submitted by Nilson Junior (nilson.junior@unila.edu.br) on 2016-05-25T23:32:09Z No. of bitstreams: 2 MKapp_PhD_2010.pdf: 2076219 bytes, checksum: db47dfe0b9ee92594b31d05a2f2e7fef (MD5) Recibo Deposito Legal_TESE_Marcelo Nepomoceno Kapp01.pdf: 205561 bytes, checksum: ac4617abe7f6d68472c1ac6ac02abd26 (MD5)
Made available in DSpace on 2016-05-25T23:32:22Z (GMT). No. of bitstreams: 2 MKapp_PhD_2010.pdf: 2076219 bytes, checksum: db47dfe0b9ee92594b31d05a2f2e7fef (MD5) Recibo Deposito Legal_TESE_Marcelo Nepomoceno Kapp01.pdf: 205561 bytes, checksum: ac4617abe7f6d68472c1ac6ac02abd26 (MD5) Previous issue date: 2010
An incremental learning system updates itself in response to incoming data without reexamining all the old data. Since classification systems capable of incrementally storing, filtering, and classifying data are economical, in terms of both space and time, which makes them immensely useful for industrial, military, and commercial purposes, interest in designing them is growing. However, the challenge with incremental learning is that classification tasks can no longer be seen as unvarying, since they can actually change with the evolution of the data. These changes in turn cause dynamic changes to occur in the classification system’s parameters If such variations are neglected, the overall performance of these systems will be compromised in the future. In this thesis, on the development of a system capable of incrementally accommodating new data and dynamically tracking new optimum system parameters for self-adaptation, we first address the optimum selection of classifiers over time. We propose a framework which combines the power of Swarm Intelligence Theory and the conventional grid-search method to progressively identify potential solutions for gradually updating training datasets. The key here is to consider the adjustment of classifier parameters as a dynamic optimization problem that depends on the data available. Specifically, it has been shown that, if the intention is to build efficient Support Vector Machine (SVM) classifiers from sources that provide data gradually and serially, then the best way to do this is to consider model selection as a dynamic process which can evolve and change over time. This means that a number of solutions are required, depending on the knowledge available about the problem and uncertainties in the data. We also investigate measures for evaluating and selecting classifier ensembles composed of SVM classifiers. The measures employed are based on two different theories (diversity and margin) commonly used to understand the success of ensembles. This study has given us valuable insights and helped us to establish confidence-based measures as a tool for the selection of classifier ensembles. The main contribution of this thesis is a dynamic optimization approach that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses over time. The approach incorporates various theories, such as dynamic Particle Swarm Optimization, incremental Support Vector Machine classifiers, change detection, and dynamic ensemble selection based on classifier confidence levels. Experiments carried out on synthetic and real-world databases demonstrate that the proposed approach outperforms the classification methods often used in incremental learning scenarios.
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Atkins, Stephen C. (Stephen Carroll). "Incremental synthesis of optimcal control laws using learning algorithms." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/46424.
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Mehta, Khushang Samir. "Using Machine Learning for Incremental Aggregation of Collaborative Rankings." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745957050039.
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Vasquez, Govea Alejandro Dizan. "Incremental learning for motion prediction of pedestrians and vehicles." Grenoble INPG, 2007. https://tel.archives-ouvertes.fr/tel-00155274.
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Le thème principal de cette thèse est la prédiction des mouvements. Ce thème est traité en partant de l'hypothèse que les piétons et les véhicules ne déplacent pas au hasard, mais ils suivent des « comportements typiques» qui peuvent être appris et utilisés ensuite dans une phase de prédiction. L'approche proposée aborde trois questions fondamentales: Modélisation: Ce travail se base en l'utilisation d'un modèle probabiliste, les modèles cachés de Markov, pour représenter les comportements typiques. Apprentissage: La thèse propose une extension aux modèles cachés de Markov qui permet d'apprendre la structure et les paramètres du modèle de façon incrémentale. Prédiction: La prédiction utilise l'inférence bayésienne exacte. Grâce aux propriétés de la structure apprise, la complexité de l'inférence est linéaire par rapport au nombre d'étatsThe main subject of this thesis is motion prediction. The problem is studied on the basis of the assumption that pedestrians and vehicles do not move randomly but follow typical "motion patterns" which may be learned and then user in a prediction phase. The approach addresses three fundamental questions: Modelling: This work is based in the utilisation of a probabilistic model, Hidden Markov Models, to represent typical motion patterns. Learning: This thesis proposes an extension to Hidden Markov Models that allows to learn the structure and parameters of the model in an incremental fashion. Prediction: Prediction is done using exact Bayesian inference. Thanks to the properties of the learned structure, the complexity of inference is linear with respect to the number of states in the model
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Martínez, Plumed Fernando. "Incremental and developmental perspectives for general-purpose learning systems." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/67269.
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[EN] The stupefying success of Artificial Intelligence (AI) for specific problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities.
Firstly, this thesis contributes with a general-purpose learning system that meets several desirable characteristics in terms of expressiveness, comprehensibility and versatility. The system works with approaches that are inherently general: inductive programming and reinforcement learning. The system does not rely on a fixed library of learning operators, but can be endowed with new ones, so being able to operate in a wide variety of contexts. This flexibility, jointly with its declarative character, makes it possible to use the system as an instrument for better understanding the role (and difficulty) of the constructs that each task requires. The learning process is also overhauled with a new developmental and lifelong approach for knowledge acquisition, consolidation and forgetting, which is necessary when bounded resources (memory and time) are considered.
Secondly, this thesis analyses whether the use of intelligence tests for AI evaluation is a much better alternative to most task-oriented evaluation approaches in AI. Accordingly, we make a review of what has been done when AI systems have been confronted against tasks taken from intelligence tests. In this regard, we scrutinise what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. Finally, the analysis of the concepts of development and incremental learning in AI systems is done at the conceptual level but also through several of these intelligence tests, providing further insight for the understanding and construction of general-purpose developing AI systems.[ES] El éxito abrumador de la Inteligencia Artificial (IA) en la resolución de tareas específicas (desde sistemas de recomendación hasta vehículos de conducción autónoma) no ha sido aún igualado con un avance similar en sistemas de IA de carácter más general enfocados en la resolución de una mayor variedad de tareas. Esta tesis aborda la creación de sistemas de IA de propósito general así como el análisis y evaluación tanto de su desarrollo como de sus capacidades cognitivas. En primer lugar, esta tesis contribuye con un sistema de aprendizaje de propósito general que reúne distintas ventajas como expresividad, comprensibilidad y versatilidad. El sistema está basado en aproximaciones de carácter inherentemente general: programación inductiva y aprendizaje por refuerzo. Además, dicho sistema se basa en una biblioteca dinámica de operadores de aprendizaje por lo que es capaz de operar en una amplia variedad de contextos. Esta flexibilidad, junto con su carácter declarativo, hace que sea posible utilizar el sistema de forma instrumental con el objetivo de facilitar la comprensión de las distintas construcciones que cada tarea requiere para ser resuelta. Por último, el proceso de aprendizaje también se revisa por medio de un enfoque evolutivo e incremental de adquisición, consolidación y olvido de conocimiento, necesario cuando se trabaja con recursos limitados (memoria y tiempo). En segundo lugar, esta tesis analiza el uso de tests de inteligencia humana para la evaluación de sistemas de IA y plantea si su uso puede constituir una alternativa válida a los enfoques actuales de evaluación de IA (más orientados a tareas). Para ello se realiza una exhaustiva revisión bibliográfica de aquellos sistemas de IA que han sido utilizados para la resolución de este tipo de problemas. Esto ha permitido analizar qué miden realmente los tests de inteligencia en los sistemas de IA, si son significativos para su evaluación, si realmente constituyen problemas complejos y, por último, si son útiles para entender la inteligencia (humana). Finalmente se analizan los conceptos de desarrollo cognitivo y aprendizaje incremental en sistemas de IA no solo a nivel conceptual, sino también por medio de estos problemas mejorando por tanto la comprensión y construcción de sistemas de propósito general evolutivos.
[CAT] L'èxit aclaparant de la Intel·ligència Artificial (IA) en la resolució de tasques específiques (des de sistemes de recomanació fins a vehicles de conducció autònoma) no ha sigut encara igualat amb un avanç similar en sistemes de IA de caràcter més general enfocats en la resolució d'una major varietat de tasques. Aquesta tesi aborda la creació de sistemes de IA de propòsit general així com l'anàlisi i avaluació tant del seu desenvolupament com de les seues capacitats cognitives. En primer lloc, aquesta tesi contribueix amb un sistema d'aprenentatge de propòsit general que reuneix diferents avantatges com ara expressivitat, comprensibilitat i versatilitat. El sistema està basat en aproximacions de caràcter inherentment general: programació inductiva i aprenentatge per reforç. A més, el sistema utilitza una biblioteca dinàmica d'operadors d'aprenentatge pel que és capaç d'operar en una àmplia varietat de contextos. Aquesta flexibilitat, juntament amb el seu caràcter declaratiu, fa que siga possible utilitzar el sistema de forma instrumental amb l'objectiu de facilitar la comprensió de les diferents construccions que cada tasca requereix per a ser resolta. Finalment, el procés d'aprenentatge també és revisat mitjançant un enfocament evolutiu i incremental d'adquisició, consolidació i oblit de coneixement, necessari quan es treballa amb recursos limitats (memòria i temps). En segon lloc, aquesta tesi analitza l'ús de tests d'intel·ligència humana per a l'avaluació de sistemes de IA i planteja si el seu ús pot constituir una alternativa vàlida als enfocaments actuals d'avaluació de IA (més orientats a tasques). Amb aquesta finalitat, es realitza una exhaustiva revisió bibliogràfica d'aquells sistemes de IA que han sigut utilitzats per a la resolució d'aquest tipus de problemes. Açò ha permès analitzar què mesuren realment els tests d'intel·ligència en els sistemes de IA, si són significatius per a la seua avaluació, si realment constitueixen problemes complexos i, finalment, si són útils per a entendre la intel·ligència (humana). Finalment s'analitzen els conceptes de desenvolupament cognitiu i aprenentatge incremental en sistemes de IA no solament a nivell conceptual, sinó també per mitjà d'aquests problemes millorant per tant la comprensió i construcció de sistemes de propòsit general evolutius.
Martínez Plumed, F. (2016). Incremental and developmental perspectives for general-purpose learning systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67269
TESIS
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Wang, Ting. "Statistical feature ordering for neural-based incremental attribute learning." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/13633/.
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In pattern recognition, better classification or regression results usually depend on highly discriminative features (also known as attributes) of datasets. Machine learning plays a significant role in the performance improvement for classification and regression. Different from the conventional machine learning approaches which train all features in one batch by some predictive algorithms like neural networks and genetic algorithms, Incremental Attribute Learning (IAL) is a novel supervised machine learning approach which gradually trains one or more features step by step. Such a strategy enables features with greater discrimination abilities to be trained in an earlier step, and avoids interference among relevant features. Previous studies have confirmed that IAL is able to generate accurate results with lower error rates. If features with different discrimination abilities are sorted in different training order, the final results may be strongly influenced. Therefore, the way to sequentially sort features with some orderings and simultaneously reduce the pattern recognition error rates based on IAL inevitably becomes an important issue in this study. Compared with the applicable yet time-consuming contribution-based feature ordering methods which were derived in previous studies, more efficient feature ordering approaches for IAL are presented to tackle classification problems in this study. In the first approach, feature orderings are calculated by statistical correlations between input and output. The second approach is based on mutual information, which employs minimal-redundancy-maximal- relevance criterion (mRMR), a well-known feature selection method, for feature ordering. The third method is improved by Fisher's Linear Discriminant (FLD). Firstly, Single Discriminability (SD) of features is presented based on FLD, which can cope with both univariate and multivariate output classification problems. Secondly, a new feature ordering metric called Accumulative Discriminability (AD) is developed based on SD. This metric is designed for IAL classification with dynamic feature dimensions. It computes the multidimensional feature discrimination ability in each step for all imported features including those imported in previous steps during the IAL training. AD can be treated as a metric for accumulative effect, while SD only measures the one-dimensional feature discrimination ability in each step. Experimental results show that all these three approaches can exhibit better performance than the conventional one-batch training method. Furthermore, the results of AD are the best of the three, because AD is much fitter for the properties of IAL, where feature number in IAL is increasing. Moreover, studies on the combination use of feature ordering and selection in IAL is also presented in this thesis. As a pre-process of machine learning for pattern recognition, sometimes feature orderings are inevitably employed together with feature selection. Experimental results show that at times these integrated approaches can obtain a better performance than non-integrated approaches yet sometimes not. Additionally, feature ordering approaches for solving regression problems are also demonstrated in this study. Experimental results show that a proper feature ordering is also one of the key elements to enhance the accuracy of the results obtained.
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Esslemont, Cameron. "A disjointed incremental discourse to visualise assurance of learning." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12440.
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This study reports on observation and validation of Assurance of Learning (AoL), as required by the University of Sydney Business School as part of its participation in the Association to Advance Collegiate Schools of Business (AACSB) International accreditation process. A design-based methodology was adopted embedding a dialogic concept mapping process to observe and assess student responses to various integrated assessment tasks against a range of pre-defined learning objectives. As the mere statement and attainment of learning goals at a program level cannot of themselves validate learning, but more reflect the “assurance of the opportunity to learn”, a broader understanding of AoL was assessed embracing curriculum management through the contemporaneous analysis of the taught curriculum with a focus on content delivery, sequencing and context; teaching proficiency through the contemporaneous identification and immediate rectification of teaching issues; and cognitive progression and retention through the contemporaneous assessment of changes in student understanding. In order to ensure focus, the validation process centred on assessment of discipline specific threshold concepts of ‘auditor independence’ and ‘true and fair view’, assessing the linking phrases in the student developed concept maps for relevance to the discipline, relevance to the question and for linguistic acceptability. Although the study focused on higher education institutions seeking accreditation with bodies like AACSB it contributes to a range of additional theories, including dialogic concept mapping, threshold concepts, the practice of assessment using Biggs SOLO Taxonomy in the analysis of linking phrases and feedback through the implementation of a dialogic concept mapping process to enhance learning through the contemporaneous rectification of misconceptions. It also provides insights for institutions involved in the teaching and assessment of English as Second Language and Second Language writers.
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Bürgel, Eduardo Jorge da Rosa. "Accelerated incremental listwise learning to rank for collaborative filtering." reponame:Repositório Institucional da UFSC, 2017. https://repositorio.ufsc.br/xmlui/handle/123456789/181254.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2017.Made available in DSpace on 2017-11-21T03:22:20Z (GMT). No. of bitstreams: 1 348587.pdf: 724704 bytes, checksum: b38ecdc2a6867c169d2e6b076ee4369e (MD5) Previous issue date: 2017
O enorme volume de informação hoje em dia aumenta a complexidade e degrada a qualidade do processo de tomada de decisão. A fim de melhorar a qualidade das decisões, os sistemas de recomendação têm sido utilizados com resultados consideráveis. Nesse contexto, a filtragem colaborativa desempenha um papel ativo em superar o problema de sobrecarga de informação. Em um cenário em que novas avaliações são recebidas constantemente, um modelo estático torna-se ultrapassado rapidamente, portanto a velocidade de atualização do modelo é um fator crítico. Propomos um método de aprendizagem de ranqueamento incremental acelerado para filtragem colaborativa. Para atingir esse objetivo, aplicamos uma técnica de aceleração a uma abordagem de aprendizado incremental para filtragem colaborativa. Resultados em conjuntos de dados reais confirmam que o algoritmo proposto é mais rápido no processo de aprendizagem mantendo a precisão do modelo.
Abstract : The enormous volume of information nowadays increases the complexity of the decision-making process and degrades the quality of decisions. In order to improve the quality of decisions, recommender systems have been applied with significant results. In this context, the collaborative filtering technique plays an active role overcoming the information overload problem. In a scenario where new ratings have been received constantly, a static model becomes outdated quickly, hence the rate of update of the model is a critical factor. We propose an accelerated incremental listwise learning to rank approach for collaborative filtering. To achieve this, we apply an acceleration technique to an incremental collaborative filtering approach. Results on real word datasets show that our proposal accelerates the learning process and keeps the accuracy of the model.
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Zuniga, Marcos. "Incremental learning of events in video using reliable information." Nice, 2008. http://www.theses.fr/2008NICE4098.
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L’objectif de cette thèse est de proposer une approche générale de compréhension de vidéo pour l’apprentissage et la reconnaissance d’événements, dans des applications du monde réel. L’approche est composée de quatre tˆaches : En premier lieu, pour chaque frame de la vidéo, une tâche de segmentation consiste à détecter les régions mobiles, lesquelles sont représentées par des boîtes englobantes qui les délimitent. En second lieu, une nouvelle méthode de classification 3D associe à chaque région mobile un label de la classe d’objet (par exemple, personne, voiture) et un parallélépipède 3D décrit par sa largeur, sa hauteur, sa longueur, sa position, son orientation, et des mesures de fiabilité associées à ces attributs. En troisième lieu, une nouvelle approche de suivi d’objets multiples utilise ces descriptions d’objet pour générer des hypothèses de suivi par rapport aux objets évoluant dans la scène. Des mesures de fiabilité associées aux attributs des objets suivis sont utilisées pour faire une sélection appropriée d’information pertinente. En dernier lieu, une nouvelle approche d’apprentissage incrémental d’événements agrège en ligne les attributs et l’information de fiabilité des objets suivis afin d’apprendre des concepts qui décrivent les événements se déroulant dans la scène. Des mesures de fiabilité sont utilisées pour focaliser le processus d’apprentissage sur l’information la plus pertinente. Simultanément, l’approche d’apprentissage d’événements reconnaît des événements associés aux objets suivis dans la scène. L’approche de suivi d’objets a été validée en utilisant des benchmarks de videosurveillance libres d’accès. L’approche complète de compréhension de vidéo a été évaluée en utilisant des vidéos obtenues d’une application réelle de maintien de personnes âgées à domicile. L’approche a été capable d’apprendre avec succès des événements associés aux trajectoires (e. G. Le changement dans la position 3D et la vitesse), la posture (e. G. Se lever, s’accroupir), et l’interaction entre objets (e. G. Une personne s’approchant d’une table), parmi d’autres événements, avec un effort minimal de configurationThe goal of this thesis is to propose a general video understanding framework for learning and recognition of events occurring in videos, for real world applications. This video understanding frameworks is composed of four tasks : first, at each video frame, a segmentation task detects the moving regions, represented by bounding boxes enclosing them. Second, a new 3D classifier associates to each moving region an object class label (e. G. Person, vehicle) and a 3D parallelepiped described by its width, height, length, position, orientation, and visual reliability measures of these attributes. Third, a new multi-object tracking algorithm uses these object descriptions to generate tracking hypotheses about the objects evolving in the scene. Finally, a new incremental event learning algorithm aggregates on-line the attributes and reliability information of the tracked objects to learn a hierarchy of concepts describing the events occurring in the scene. Reliability measures are used to focus the learning process on the most valuable information. Simultaneously, the event learning approach recognizes the events associated to the objects evolving in the scene. The tracking approach has been validated using video-surveillance benchmarks publicly accessible. The complete video understanding framework has been evaluated with videos for a real elderly care application. The framework has been able to successfully learn events related to trajectory (e. G. Change in 3D position and velocity), posture (e. G. Standing up, crouching), and object interaction (e. G. Person approaching to a table), among other events, with a minimal configuration effort
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Craye, Céline. "Intrinsic motivation mecanisms for incremental learning of visual saliency." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY006/document.
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La conception de systèmes de perception autonomes, tels que des robots capables d’accomplir un ensemble de tâches de manière sûre et sans assistance humaine, est l’un des grands défis de notre siècle. Pour ce faire, la robotique développementale propose de concevoir des robots qui, comme des enfants, auraient la faculté d’apprendre directement par interaction avec leur environnement. Nous avons dans cette thèse exploré de telles possibilités en se limitant à l’apprentissage de la localisation des objets d’intérêt (ou objets saillants) dans l’environnement du robot.Pour ce faire, nous présentons dans ces travaux un mécanisme capable d’apprendre la saillance visuelle directement sur un robot, puis d’utiliser le modèle appris de la sorte pour localiser des objets saillants dans son environnement. Cette méthode a l’avantage de permettre la création de modèles spécialisés pour l’environnement du robot et les tâches qu’il doit accomplir, tout en restant flexible à d’éventuelles nouveautés ou modifications de l’environnement.De plus, afin de permettre un apprentissage efficace et de qualité, nous avons développé des stratégies d’explorations basées sur les motivations intrinsèques, très utilisées en robotique développementale. Nous avons notamment adapté l’algorithme IAC à l’apprentissage de la saillance visuelle, et en avons conçu une extension, RL-IAC, pour permettre une exploration efficace sur un robot mobile. Afin de vérifier et d’analyser les performances de nos algorithmes, nous avons réalisé des évaluations sur plusieurs plateformes robotiques dont une plateforme fovéale et un robot mobile, ainsi que sur des bases de données publiquesConceiving autonomous perceptual systems, such as robots able to accomplish a set of tasks in a safe way, without any human assistance, is one of the biggest challenge of the century. To this end, the developmental robotics suggests to conceive robots able to learn by interacting directly with their environment, just like children would. This thesis is exploring such possibility while restricting the problem to the one of localizing objects of interest (or salient objects) within the robot’s environment.For that, we present in this work a mechanism able to learn visual saliency directly on a robot, then to use the learned model so as to localize salient objects within their environment. The advantage of this method is the creation of models dedicated to the robot’s environment and tasks it should be asked to accomplish, while remaining flexible to any change or novelty in the environment.Furthermore, we have developed exploration strategies based on intrinsic motivations, widely used in developmental robotics, to enable efficient learning of good quality. In particular, we adapted the IAC algorithm to visual saliency leanring, and proposed an extension, RL-IAC to allow an efficient exploration on mobile robots.In order to verify and analyze the performance of our algorithms, we have carried out various experiments on several robotics platforms, including a foveated system and a mobile robot, as well as publicly available datasets
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Craye, Céline. "Intrinsic motivation mecanisms for incremental learning of visual saliency." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY006.
Full textAbstract:
La conception de systèmes de perception autonomes, tels que des robots capables d’accomplir un ensemble de tâches de manière sûre et sans assistance humaine, est l’un des grands défis de notre siècle. Pour ce faire, la robotique développementale propose de concevoir des robots qui, comme des enfants, auraient la faculté d’apprendre directement par interaction avec leur environnement. Nous avons dans cette thèse exploré de telles possibilités en se limitant à l’apprentissage de la localisation des objets d’intérêt (ou objets saillants) dans l’environnement du robot.Pour ce faire, nous présentons dans ces travaux un mécanisme capable d’apprendre la saillance visuelle directement sur un robot, puis d’utiliser le modèle appris de la sorte pour localiser des objets saillants dans son environnement. Cette méthode a l’avantage de permettre la création de modèles spécialisés pour l’environnement du robot et les tâches qu’il doit accomplir, tout en restant flexible à d’éventuelles nouveautés ou modifications de l’environnement.De plus, afin de permettre un apprentissage efficace et de qualité, nous avons développé des stratégies d’explorations basées sur les motivations intrinsèques, très utilisées en robotique développementale. Nous avons notamment adapté l’algorithme IAC à l’apprentissage de la saillance visuelle, et en avons conçu une extension, RL-IAC, pour permettre une exploration efficace sur un robot mobile. Afin de vérifier et d’analyser les performances de nos algorithmes, nous avons réalisé des évaluations sur plusieurs plateformes robotiques dont une plateforme fovéale et un robot mobile, ainsi que sur des bases de données publiquesConceiving autonomous perceptual systems, such as robots able to accomplish a set of tasks in a safe way, without any human assistance, is one of the biggest challenge of the century. To this end, the developmental robotics suggests to conceive robots able to learn by interacting directly with their environment, just like children would. This thesis is exploring such possibility while restricting the problem to the one of localizing objects of interest (or salient objects) within the robot’s environment.For that, we present in this work a mechanism able to learn visual saliency directly on a robot, then to use the learned model so as to localize salient objects within their environment. The advantage of this method is the creation of models dedicated to the robot’s environment and tasks it should be asked to accomplish, while remaining flexible to any change or novelty in the environment.Furthermore, we have developed exploration strategies based on intrinsic motivations, widely used in developmental robotics, to enable efficient learning of good quality. In particular, we adapted the IAC algorithm to visual saliency leanring, and proposed an extension, RL-IAC to allow an efficient exploration on mobile robots.In order to verify and analyze the performance of our algorithms, we have carried out various experiments on several robotics platforms, including a foveated system and a mobile robot, as well as publicly available datasets
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Haseeb, Abdul. "Interoperability Infrastructure and Incremental learning for unreliable heterogeneous communicating Systems." Licentiate thesis, KTH, Electronic, Computer and Software Systems, ECS, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11352.
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In a broader sense the main research objective of this thesis (and ongoing research work) is distributed knowledge management for mobile dynamic systems. But the primary focus and presented work focuses on communication/interoperability of heterogeneous entities in an infrastructure less paradigm, a distributed resource manipulation infrastructure and distributed learning in the absence of global knowledge. The research objectives achieved discover the design aspects of heterogeneous distributed knowledge systems towards establishing a seamless integration. This thesis doesn’t cover all aspects in this work; rather focuses on interoperability and distributed learning.
Firstly a discussion on the issues in knowledge management for swarm of heterogeneous entities is presented. This is done in a broader and rather abstract fashion to provide an insight of motivation for interoperability and distributed learning towards knowledge management. Moreover this will also serve the reader to understand the ongoing work and research activities in much broader perspective.
Primary focus of this thesis is communication/interoperability of heterogeneous entities in an infrastructure less paradigm, a distributed resource manipulation infrastructure and distributed learning in the absence of global knowledge. In dynamic environments for mobile autonomous systems such as robot swarms or mobile software agents there is a need for autonomic publishing and discovery of resources and just-in-time integration for on-the-fly service consumption without any a priori knowledge. SOA (Service-Oriented Architecture) serves the purpose of resource reuse and sharing of services different entities. Web services (a SOA manifestation) achieves these objectives but its exploitation in dynamic environments, where the communication infrastructure is lacking, requires a considerable research. Generally Web services are exploited in stable client-server paradigms, which is a pressing assumption when dynamic distributed systems are considered. UDDI (Universal Description Discovery and Integration) is the main pediment in the exploitation of Web services in distributed control and dynamic natured systems. UDDI can be considered as a directory for publication and discovery of categorized Web services but assumes a centralized registry; even if distributed registries and associated mechanism are employed problems of collaborative communication in infrastructure less paradigms are ignored.
Towards interoperability main contribution this thesis is a mediator-based distributed Web services discovery and invocation middleware, which provides a collaborative and decentralized services discovery and management middleware for infrastructure-less mobile dynamic systems with heterogeneous communication capabilities. Heterogeneity of communication capabilities is abstracted in middleware by a conceptual classification of computing entities on the basis of their communication capabilities and communication issues are resolved via conceptual overlay formation for query propagation in system.
The proposed and developed middleware has not only been evaluated extensively using Player Stage simulator but also been applied in physical robot swarms. Experimental validations analyze the results in different communication modes i. active and ii. passive mode of communication with and without shared resource conflict resolution. I analyze discoverable Web services with respect to time, services available in complete view of cluster and the impact and resultant improvements in distributed Web services discovery by using caching and semantics.
Second part of this thesis focuses on distributed learning in the absence of global information. This thesis takes the argument of defeasibility (common-sense inference) as the basis of intelligence in human-beings, in which conclusions/inferences are drawn and refuted at the same time as more information becomes available. The ability of common-sense reasoning to adapt to dynamic environments and reasoning with uncertainty in the absence of global information seems to be best fit for distributed learning for dynamic systems.
This thesis, thus, overviews epistemic cognition in human beings, which motivates the need of a similar epistemic cognitive solution in fabricated systems and considers formal concept analysis as a case for incremental and distributed learning of formal concepts. Thesis also presents a representational schema for underlying logic formalism and formal concepts. An algorithm for incremental learning and its use-case for robotic navigation, in which robots incrementally learn formal concepts and perform common-sense reasoning for their intelligent navigation, is also presented. Moreover elaboration of the logic formalism employed and details of implementation of developed defeasible reasoning engine is given in the latter half of this thesis.
In summary, the research results and achievements described in this thesis focus on interoperability and distributed learning for heterogeneous distributed knowledge systems which contributes towards establishing a seamless integration in mobile dynamic systems.
QC 20100614
ROBOSWARM EU FP6
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Wang, Jin. "An Incremental Multilinear System for Human Face Learning and Recognition." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/312.
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This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.
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Keysermann, Matthias Ulrich. "An incremental clustering and associative learning architecture for intelligent robotics." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2961.
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The ability to learn from the environment and memorise the acquired knowledge is essential for robots to become autonomous and versatile artificial companions. This thesis proposes a novel learning and memory architecture for robots, which performs associative learning and recall of sensory and actuator patterns. The approach avoids the inclusion of task-specific expert knowledge and can deal with any kind of multi-dimensional real-valued data, apart from being tolerant to noise and supporting incremental learning. The proposed architecture integrates two machine learning methods: a topology learning algorithm that performs incremental clustering, and an associative memory model that learns relationship information based on the co-occurrence of inputs. The evaluations of both the topology learning algorithm and the associative memory model involved the memorisation of high-dimensional visual data as well as the association of symbolic data, presented simultaneously and sequentially. Moreover, the document analyses the results of two experiments in which the entire architecture was evaluated regarding its associative and incremental learning capabilities. One experiment comprised an incremental learning task with visual patterns and text labels, which was performed both in a simulated scenario and with a real robot. In a second experiment a robot learned to recognise visual patterns in the form of road signs and associated them with di erent con gurations of its arm joints. The thesis also discusses several learning-related aspects of the architecture and highlights strengths and weaknesses of the proposed approach. The developed architecture and corresponding ndings contribute to the domains of machine learning and intelligent robotics.
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Liu, Wen. "Incremental Learning and Online-Style SVM for Traffic Light Classification." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/1216.
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Training a large dataset has become a serious issue for researchers because it requires large memories and can take a long time for computing. People are trying to process large scale dataset not only by changing programming model, such as using MapReduce and Hadoop, but also by designing new algorithms that can retain performance with less complexity and runtime. In this thesis, we present implementations of incremental learning and online learning methods to classify a large traffic light dataset for traffic light recognition. The introduction part includes the concepts and related works of incremental learning and online learning. The main algorithm is a modification of IMORL incremental learning model to enhance its performance over the learning process of our application. Then we briefly discuss how the traffic light recognition algorithm works and the problem we encounter during training. Rather than focusing on incremental learning, which uses batch to batch data during training procedure, we introduce Pegasos, an online style primal gradient-based support vector machine method. The performance of Pegasos for classification is extraordinary and the number of instances it uses for training is relatively small. Therefore, Pegasos is the recommended solution to the large dataset training problem.
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Lughofer, Edwin. "Evolving fuzzy models incremental learning, interpretability, and stability issues, applications." Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/989191559/04.
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Ling, TR. "An Incremental Learning Method for Data Mining from Large Databases." Thesis, Honours thesis, University of Tasmania, 2006. https://eprints.utas.edu.au/793/1/trling_Honours_Thesis.pdf.
Full textAbstract:
Knowledge Discovery techniques seek to find new information about a domain
through a combination of existing domain knowledge and data examples from the
domain. These techniques can either be manually performed by an expert, or
automated using software algorithms (Machine Learning). However some domains,
such as the clinical field of Lung Function testing, contain volumes of data too vast
and detailed for manual analysis to be effective, and existing knowledge too complex
for Machine Learning algorithms to be able to adequately discover relevant
knowledge. In many cases this data is also unclassified, with no previous analysis
having been performed. A better approach for these domains might be to involve a
human expert, taking advantage of their expertise to guide the process, and to use
Machine Learning techniques to assist the expert in discovering new and meaningful
relationships in the data. It is hypothesised that Knowledge Acquisition methods
would provide a strong basis for such a Knowledge Discovery method, particularly
methods which can provide incremental verification and validation of knowledge as
it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
Down Rules) Knowledge Acquisition process can be adapted to develop a new
Knowledge Discovery method, Exposed MCRDR, and tests this method in the
domain of Lung Function. Preliminary results suggest that the EMCRDR method can
be successfully applied to discover new knowledge in a complex domain, and reveal
many potential areas of study and development for the MCRDR method.
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Kunwar, Rituraj. "Incremental / Online Learning and its Application to Handwritten Character Recognition." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/366964.
Full textAbstract:
In real world scenarios where we use machine learning algorithms, we often have to deal with cases where input data changes its nature with time. In order to maintain the accuracy of the learning algorithm, we frequently have to retrain our learning system, thereby making the system inconvenient and unreliable. This problem can be solved by using learning algorithms which can learn continuously with time (incremental/ online learning). Another common problem of real-world learning scenarios that we often have to deal with is acquiring large amounts of data which is expensive and time consuming. Semi-supervised learning is the machine learning paradigm concerned with utilizing unlabeled data to improve the precision of classifier or regressor. Unlabeled data is a powerful and easily available resource and it should be utilized to build an accurate learning system. It has often been observed that there is a vast amount of redundancy in any huge, real-time database and it is not advisable to process every redundant sample to gain the same (already acquired) knowledge. Active learning is the learning setting which can handle this issue. Therefore in this research we propose an online semi-supervised learning framework which can learn actively. We have proposed an "online semi-supervised Random
Naive Bayes (RNB)" classifier and as the name implies it can learn in an online manner and make use of both labeled and unlabeled data to learn. In order to boost accuracy we improved the network structure of NB (using Bayes net) to propose an Augmented Naive Bayes (ANB) classifier and achieved a substantial jump in accuracy. In order to reduce the processing of redundant data and achieve faster convergence of learning, we proposed to conduct incremental semi-supervised learning in active manner. We applied the proposed methods on the "Tamil script handwritten character recognition"
problem and have obtained favorable results. Experimental results prove that our proposed online classifiers does as well as and sometimes better than its batch learning counterpart. And active learning helps to achieve learning convergence with much less number of samples.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
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Byun, Byungki. "On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43597.
Full textAbstract:
This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having a large number of labeled images is crucial. However, the size of the training set is often limited due to the cost required for generating concept labels associated with objects in a large quantity of images. To address this issue, in this research, we propose to incrementally incorporate unlabeled samples into a learning process to enhance concept models originally learned with a small number of labeled samples. To tackle the sub-optimality problem of conventional techniques, the proposed incremental learning framework selects unlabeled samples based on an expected error reduction function that measures contributions of the unlabeled samples based on their ability to increase the modeling accuracy. To improve the convergence property of the proposed incremental learning framework, we further propose a multi-view learning approach that makes use of multiple features such as color, texture, etc., of images when including unlabeled samples. For robustness to mismatches between training and testing conditions, a discriminative learning algorithm, namely a kernelized maximal- figure-of-merit (kMFoM) learning approach is also developed. Combining individual techniques, we conduct a set of experiments on various image concept modeling problems, such as handwritten digit recognition, object recognition, and image spam detection to highlight the effectiveness of the proposed framework.
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Lourens, Tobie. "Using population-based incremental learning to optimize feasible distribution logistic solutions." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019/1097.
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Muhlbaier, Michael David. "Boosted ensemble algorithm strategically trained for the incremental learning of unbalanced data /." Full text available online, 2006. http://www.lib.rowan.edu/find/theses.
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Rodrigues, Thiago Fredes. "A probabilistic and incremental model for online classification of documents : DV-INBC." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/142171.
Full textAbstract:
Recentemente, houve um aumento rápido na criação e disponibilidade de repositórios de dados, o que foi percebido nas áreas de Mineração de Dados e Aprendizagem de Máquina. Este fato deve-se principalmente à rápida criação de tais dados em redes sociais. Uma grande parte destes dados é feita de texto, e a informação armazenada neles pode descrever desde perfis de usuários a temas comuns em documentos como política, esportes e ciência, informação bastante útil para várias aplicações. Como muitos destes dados são criados em fluxos, é desejável a criação de algoritmos com capacidade de atuar em grande escala e também de forma on-line, já que tarefas como organização e exploração de grandes coleções de dados seriam beneficiadas por eles. Nesta dissertação um modelo probabilístico, on-line e incremental é apresentado, como um esforço em resolver o problema apresentado. O algoritmo possui o nome DV-INBC e é uma extensão ao algoritmo INBC. As duas principais características do DV-INBC são: a necessidade de apenas uma iteração pelos dados de treino para criar um modelo que os represente; não é necessário saber o vocabulário dos dados a priori. Logo, pouco conhecimento sobre o fluxo de dados é necessário. Para avaliar a performance do algoritmo, são apresentados testes usando datasets populares.Recently the fields of Data Mining and Machine Learning have seen a rapid increase in the creation and availability of data repositories. This is mainly due to its rapid creation in social networks. Also, a large part of those data is made of text documents. The information stored in such texts can range from a description of a user profile to common textual topics such as politics, sports and science, information very useful for many applications. Besides, since many of this data are created in streams, scalable and on-line algorithms are desired, because tasks like organization and exploration of large document collections would be benefited by them. In this thesis an incremental, on-line and probabilistic model for document classification is presented, as an effort of tackling this problem. The algorithm is called DV-INBC and is an extension to the INBC algorithm. The two main characteristics of DV-INBC are: only a single scan over the data is necessary to create a model of it; the data vocabulary need not to be known a priori. Therefore, little knowledge about the data stream is needed. To assess its performance, tests using well known datasets are presented.
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Roscher, Ribana [Verfasser]. "Sequential learning using incremental import vector machines for semantic segmentation / Ribana Roscher." Bonn : Universitäts- und Landesbibliothek Bonn, 2012. http://d-nb.info/1043056424/34.
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