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Dissertations / Theses on the topic 'The Knowledge Machine'
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Ingevall, Markus. "Extending the Knowledge Machine." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2427.
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This master's thesis deals with a frame-based knowledge representa- tion language and system called The Knowledge Machine (KM), de- veloped by Peter Clark and Bruce Porter at the University of Texas at Austin. The purpose of the thesis is to show a number of ways of changing and extending KM to handle larger classes of reasoning tasks associated with reasoning about actions and change.
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Gispert, Ramis Adrià. "Introducing linguistic knowledge into statistical machine translation." Doctoral thesis, Universitat Politècnica de Catalunya, 2007. http://hdl.handle.net/10803/6902.
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Aquesta tesi està dedicada a l'estudi de la utilització de informació morfosintàctica en el marc dels sistemes de traducció estocàstica, amb l'objectiu de millorar-ne la qualitat a través de la incorporació de informació lingüística més enllà del nivell simbòlic superficial de les paraules.El sistema de traducció estocàstica utilitzat en aquest treball segueix un enfocament basat en tuples, unitats bilingües que permeten estimar un model de traducció de probabilitat conjunta per mitjà de la combinació, dins un entorn log-linial, de cadenes d'n-grames i funcions característiques addicionals. Es presenta un estudi detallat d'aquesta aproximació, que inclou la seva transformació des d'una implementació d'X-grames en autòmats d'estats finits, més orientada a la traducció de veu, cap a l'actual solució d'n-grames orientada a la traducció de text de gran vocabulari. La tesi estudia també les fases d'entrenament i decodificació, així com el rendiment per a diferents tasques (variant el tamany dels corpora o el parell d'idiomes) i els principals problemes reflectits en les anàlisis d'error.
La tesis també investiga la incorporació de informació lingüística específicament en aliniament per paraules. Es proposa l'extensió mitjançant classificació de formes verbals d'un algorisme d'aliniament paraula a paraula basat en co-ocurrències, amb resultats positius. Així mateix, s'avalua de forma empírica l'impacte en qualitat d'aliniament i de traducció que s'obté mitjançant l'etiquetatge morfològic, la lematització, la classificació de formes verbals i el truncament o stemming del text paral·lel.
Pel que fa al model de traducció, es proposa un model de tractament de les formes verbals per mitjà d'un model de instanciació addicional, i es realitzen experiments en la direcció d'anglès a castellà. La tesi també introdueix un model de llenguatge d'etiquetes morfològiques del destí per tal d'abordar problemes de concordança. Finalment, s'estudia l'impacte de la derivació morfològica en la formulació de la traducció estocàstica mitjançant n-grames, avaluant empíricament el possible guany derivat d'estratègies de reducció morfològica.
This Ph.D. thesis dissertation addresses the use of morphosyntactic information in order to improve the performance of Statistical Machine Translation (SMT) systems, providing them with additional linguistic information beyond the surface level of words from parallel corpora.
The statistical machine translation system in this work here follows a tuple-based approach, modelling joint-probability translation models via log-linear combination of bilingual n-grams with additional feature functions. A detailed study of the approach is conducted. This includes its initial development from a speech-oriented Finite-State Transducer architecture implementing X-grams towards a large-vocabulary text-oriented n-grams implementation, training and decoding particularities, portability across language pairs and tasks, and main difficulties as revealed in error analyses.
The use of linguistic knowledge to improve word alignment quality is also studied. A cooccurrence-based one-to-one word alignment algorithm is extended with verb form classification with successful results. Additionally, we evaluate the impact in word alignment and translation quality of Part-Of-Speech, base form, verb form classification and stemming on state-of-art word alignment tools.
Furthermore, the thesis proposes a translation model tackling verb form generation through an additional verb instance model, reporting experiments in English-to-Spanish tasks. Disagreement is addressed via incorporating a target Part-Of-Speech language model. Finally, we study the impact of morphology derivation on Ngram-based SMT formulation, empirically evaluating the quality gain that is to be gained via morphology reduction.
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Berry, David T. "A knowledge-based framework for machine vision." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1022.
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Zbib, Rabih M. (Rabih Mohamed) 1974. "Using linguistic knowledge in statistical machine translation." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62391.
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Thesis (Ph. D. in Information Technology)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 153-162).
In this thesis, we present methods for using linguistically motivated information to enhance the performance of statistical machine translation (SMT). One of the advantages of the statistical approach to machine translation is that it is largely language-agnostic. Machine learning models are used to automatically learn translation patterns from data. SMT can, however, be improved by using linguistic knowledge to address specific areas of the translation process, where translations would be hard to learn fully automatically. We present methods that use linguistic knowledge at various levels to improve statistical machine translation, focusing on Arabic-English translation as a case study. In the first part, morphological information is used to preprocess the Arabic text for Arabic-to-English and English-to-Arabic translation, which reduces the gap in the complexity of the morphology between Arabic and English. The second method addresses the issue of long-distance reordering in translation to account for the difference in the syntax of the two languages. In the third part, we show how additional local context information on the source side is incorporated, which helps reduce lexical ambiguity. Two methods are proposed for using binary decision trees to control the amount of context information introduced. These methods are successfully applied to the use of diacritized Arabic source in Arabic-to-English translation. The final method combines the outputs of an SMT system and a Rule-based MT (RBMT) system, taking advantage of the flexibility of the statistical approach and the rich linguistic knowledge embedded in the rule-based MT system.
by Rabih M. Zbib.
Ph.D.in Information Technology
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Hall, Joseph Alexander. "Machine learning for control : incorporating prior knowledge." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/283930.
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Hasan, Irfan. "Machine learning techniques for automated knowledge acquisition in intelligent knowledge-based systems." Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1991. http://www.kutztown.edu/library/services/remote_access.asp.
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Thesis (M.S.)--Kutztown University of Pennsylvania, 1991.Source: Masters Abstracts International, Volume: 45-06, page: 3187. Abstract precedes thesis as [2] preliminary leaves. Typescript. Includes bibliographical references (leaves 102-104).
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Mallen, Jason. "Utilising incomplete domain knowledge in an information theoretic guided inductive knowledge discovery algorithm." Thesis, University of Portsmouth, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295773.
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Snyders, Sean. "Inductive machine learning bias in knowledge-based neurocomputing." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53463.
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Thesis (MSc) -- Stellenbosch University , 2003.ENGLISH ABSTRACT: The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving real-world problems. This paradigm named knowledge-based neurocomputing, provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guides network training, and to extract refined knowledge from trained neural networks. The role of neural networks then becomes that of knowledge refinement. It thus provides a methodology for dealing with uncertainty in the initial domain theory. In this thesis, we address several advantages of this paradigm and propose a solution for the open question of determining the strength of this learning, or inductive, bias. We develop a heuristic for determining the strength of the inductive bias that takes the network architecture, the prior knowledge, the learning method, and the training data into consideration. We apply this heuristic to well-known synthetic problems as well as published difficult real-world problems in the domain of molecular biology and medical diagnoses. We found that, not only do the networks trained with this adaptive inductive bias show superior performance over networks trained with the standard method of determining the strength of the inductive bias, but that the extracted refined knowledge from these trained networks deliver more concise and accurate domain theories.
AFRIKAANSE OPSOMMING: Die integrasie van simboliese kennis met kunsmatige neurale netwerke word 'n toenemende gewilde paradigma om reelewereldse probleme op te los. Hierdie paradigma genoem, kennis-gebaseerde neurokomputasie, verskaf die vermoe om vooraf kennis te gebruik om die netwerkargitektuur te bepaal, om a subversameling van gewigte te programeer om 'n leersydigheid te induseer wat netwerkopleiding lei, en om verfynde kennis van geleerde netwerke te kan ontsluit. Die rol van neurale netwerke word dan die van kennisverfyning. Dit verskaf dus 'n metodologie vir die behandeling van onsekerheid in die aanvangsdomeinteorie. In hierdie tesis adresseer ons verskeie voordele wat bevat is in hierdie paradigma en stel ons 'n oplossing voor vir die oop vraag om die gewig van hierdie leer-, of induktiewe sydigheid te bepaal. Ons ontwikkel 'n heuristiek vir die bepaling van die induktiewe sydigheid wat die netwerkargitektuur, die aanvangskennis, die leermetode, en die data vir die leer proses in ag neem. Ons pas hierdie heuristiek toe op bekende sintetiese probleme so weI as op gepubliseerde moeilike reelewereldse probleme in die gebied van molekulere biologie en mediese diagnostiek. Ons bevind dat, nie alleenlik vertoon die netwerke wat geleer is met die adaptiewe induktiewe sydigheid superieure verrigting bo die netwerke wat geleer is met die standaardmetode om die gewig van die induktiewe sydigheid te bepaal nie, maar ook dat die verfynde kennis wat ontsluit is uit hierdie geleerde netwerke meer bondige en akkurate domeinteorie lewer.
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Lazzarini, Nicola. "Knowledge extraction from biomedical data using machine learning." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3839.
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Thanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.
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Pickard, Nigel Brougham. "The development of fuzzy knowledge base for machine monitoring." Thesis, University of Bristol, 1989. http://hdl.handle.net/1983/f4b76b81-7da1-494c-9cfa-3f21d62f9a48.
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Crouch, Ingrid W. M. "A knowledge-based simulation optimization system with machine learning." Diss., Virginia Tech, 1992. http://hdl.handle.net/10919/37245.
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Hassani, Kaveh. "Commonsense Knowledge for 3D Modeling: A Machine Learning Approach." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36165.
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Common‒sense knowledge is a collection of non‒expert and agreed‒upon facts and information about the world shared among most people past early childhood based on their experiences. It includes uses of objects, their properties, parts and materials, their locations, spatial arrangements among them; location and duration of events; arguments, preconditions and effects of actions; urges and emotions of people, etc. In creating 3D worlds and especially text‒ to‒scene and text‒to‒animation systems, this knowledge is essential to eliminate the tedious and low‒level tasks, thus allowing users to focus on their creativity and imagination. We address tasks related to five categories of common‒sense knowledge that is required by such systems including: (1) spatial role labeling to automatically identify and annotate a set of spatial signals within a scene description in natural language; (2) grounding spatial relations to automatically position an object in 3D world; (3) inferring spatial relations to extract symbolic spatial relation between objects to answer questions regarding a 3D world; (4) recommending objects and their relative spatial relations given a recent manipulated object to auto‒complete a scene design; (5) learning physical attributes (e.g., size, weight, and speed) of objects and their corresponding distribution. We approach these tasks by using deep learning and probabilistic graphical models and exploit existing datasets and web content to learn the corresponding common‒sense knowledge.
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Wusteman, Judith. "EBKAT : an explanation-based knowledge acquisition tool." Thesis, University of Exeter, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280682.
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Youn, Bong-Soo. "Intelligent knowledge acquisition system /." Online version of thesis, 1989. http://hdl.handle.net/1850/10444.
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Desimone, Roberto V. "Learning control knowledge within an explanation-based learning framework." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/18827.
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Mao, Yi. "Domain knowledge, uncertainty, and parameter constraints." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37295.
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Tang, Qiao. "Knowledge management using machine learning, natural language processing and ontology." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56067/.
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This research developed a concept indexing framework which systematically integrates machine learning, natural language processing and ontology technologies to facilitate knowledge acquisition, extraction and organisation. The research reported in this thesis focuses first on the conceptual model of concept indexing, which represents knowledge as entities and concepts. Then the thesis outlines its benefits and the system architecture using this conceptual model. Next, the thesis presents a knowledge acquisition framework using machine learning in focused crawling Web content to enable automatic knowledge acquisition. Then, the thesis presents two language resources developed to enable ontology tagging, which are: an ontology dictionary and an ontologically tagged corpus. The ontologically tagged corpus is created using a heuristic algorithm developed in the thesis. Next, the ontology tagging algorithm is developed with the ontology dictionary and the ontologically tagged corpus to enable ontology tagging. Finally, the thesis presents the conceptual model, the system architecture, and the prototype system using concept indexing developed to facilitate knowledge acquisition, extraction and organisation. The solutions proposed in the thesis are illustrated with examples based on a prototype system developed in this thesis.
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Farrash, Majed. "Machine learning ensemble method for discovering knowledge from big data." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59367/.
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Big data, generated from various business internet and social media activities, has become a big challenge to researchers in the field of machine learning and data mining to develop new methods and techniques for analysing big data effectively and efficiently. Ensemble methods represent an attractive approach in dealing with the problem of mining large datasets because of their accuracy and ability of utilizing the divide-and-conquer mechanism in parallel computing environments. This research proposes a machine learning ensemble framework and implements it in a high performance computing environment. This research begins by identifying and categorising the effects of partitioned data subset size on ensemble accuracy when dealing with very large training datasets. Then an algorithm is developed to ascertain the patterns of the relationship between ensemble accuracy and the size of partitioned data subsets. The research concludes with the development of a selective modelling algorithm, which is an efficient alternative to static model selection methods for big datasets. The results show that maximising the size of partitioned data subsets does not necessarily improve the performance of an ensemble of classifiers that deal with large datasets. Identifying the patterns exhibited by the relationship between ensemble accuracy and partitioned data subset size facilitates the determination of the best subset size for partitioning huge training datasets. Finally, traditional model selection is inefficient in cases wherein large datasets are involved.
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Goebel, Randy. "A logic data model for the machine representation of knowledge." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25799.
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DLOG is a logic-based data model developed to show how logic-programming can combine contributions of Data Base Management (DBM) and Artificial Intelligence (AI). The DLOG specification includes a language syntax, a proof (or query evaluation) procedure, a description of the language's semantics,
and a specification of the relationships between assertions, queries, and application databases. DLOG's data description language is the Horn clause subset of first order logic [Kowalski79, Kowalski81], augmented with descriptive terms and non-Horn integrity constraints. The descriptive terms are motivated by AI representation language ideas, specifically, the descriptive terms of the KRL language [Bobrow77]. A similar facility based on logical descriptions is provided in DLOG. DLOG permits the use of definite and indefinite descriptions of individuals and sets in queries and assertions. The meaning of DLOG's extended language is specified as Horn clauses that describe the relation between the basic language and the extensions. The experimental implementation is a Prolog program derived from that specification. The DLOG implementation relies on an extension to the standard Prolog proof procedure. This includes a "unification" procedure that matches embedded terms by recursively invoking the DLOG proof procedure (cf. LOGLISP [Robinson82]). The experimental system includes Prolog implementations of traditional database facilities (e.g., transactions, integrity constraints, data dictionaries, data manipulation language facilities), and an idea for using logic as the basis for heuristic interpretation of queries. This heuristic uses a notion of partial, match or sub-proof to produce assumptions under which plausible query answers can be derived. The experimental DLOG knowledge base management system is exercised by describing an undergraduate degree program. The example application is a description of the Bachelor of Computer Science degree requirements at The University of British Columbia. This application demonstrates how DLOG's descriptive terms provide a concise description of degree program knowledge, and how that knowledge is used to specify student programs and select program options.Science, Faculty of
Computer Science, Department of
Graduate
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Khalili, K. "Enhancing vision data using prior knowledge for assembly applications." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360432.
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Kulkarni, Praveen. "Knowledge transfer for image understanding." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC207/document.
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Le Transfert de Connaissance (Knowledge Transfer or Transfer Learning) est une solution prometteuse au difficile problème de l’apprentissage des réseaux profonds au moyen de bases d’apprentissage de petite taille, en présence d’une grande variabilité visuelle intra-classe. Dans ce travail, nous reprenons ce paradigme, dans le but d’étendre les capacités des CNN les plus récents au problème de la classification. Dans un premier temps, nous proposons plusieurs techniques permettant, lors de l’apprentissage et de la prédiction, une réduction des ressources nécessaires – une limitation connue des CNN. (i) En utilisant une méthode hybride combinant des techniques classiques comme des Bag-Of-Words (BoW) avec des CNN. (iv) En introduisant une nouvelle méthode d’agrégation intégrée à une structure de type CNN ainsi qu’un modèle non-linéaire s’appuyant sur des parties de l’image. La contribution clé est, finalement, une technique capable d’isoler les régions des images utiles pour une représentation locale. De plus, nous proposons une méthode nouvelle pour apprendre une représentation structurée des coefficients des réseaux de neurones. Nous présentons des résultats sur des jeux de données difficiles, ainsi que des comparaisons avec des méthodes concurrentes récentes. Nous prouvons que les méthodes proposées s’étendent à d’autres tâches de reconnaissance visuelles comme la classification d’objets, de scènes ou d’actionsKnowledge transfer is a promising solution for the difficult problem of training deep convolutional neural nets (CNNs) using only small size training datasets with a high intra-class visual variability. In this thesis work, we explore this paradigm to extend the ability of state-of-the-art CNNs for image classification.First, we propose several effective techniques to reduce the training and test-time computational burden associated to CNNs:(i) Using a hybrid method to combine conventional, unsupervised aggregators such as Bag-of-Words (BoW) with CNNs;(ii) Introducing a novel pooling methods within a CNN framework along with non-linear part-based models. The key contribution lies in a technique able to discover useful regions per image involved in the pooling of local representations;In addition, we also propose a novel method to learn the structure of weights in deep neural networks. Experiments are run on challenging datasets with comparisons against state-of-the-art methods. The methods proposed are shown to generalize to different visual recognition tasks, such as object, scene or action classification
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Pipe, Anthony Graham. "Reinforcement learning and knowledge transformation in mobile robotics." Thesis, University of the West of England, Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364077.
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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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Richardson, Matthew. "Learning and inference in collective knowledge bases /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/6926.
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Bollacker, Kurt Dewitt. "A supra-classifier framework for knowledge reuse /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Cho, Tai-Hoon. "A knowledge-based machine vision system for automated industrial web inspection." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-134615/.
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Al-Awadhi, Waleed. "Integrating machine grouping and layout by using knowledge based system approach." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242982.
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Tuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.
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Abstract Knowledge discovery (KD) technology is used to extract knowledge from large quantities of digital data in an automated fashion. The established process model represents the KD process in a linear and technology-centered manner, as a sequence of transformations that refine raw data into more and more abstract and distilled representations. Any actual KD process, however, has aspects that are not adequately covered by this model. In particular, some of the most important actors in the process are not technological but human, and the operations associated with these actors are interactive rather than sequential in nature. This thesis proposes an augmentation of the established model that addresses this neglected dimension of the KD process. The proposed process model is composed of three sub-models: a data model, a workflow model, and an architectural model. Each sub-model views the KD process from a different angle: the data model examines the process from the perspective of different states of data and transformations that convert data from one state to another, the workflow model describes the actors of the process and the interactions between them, and the architectural model guides the design of software for the execution of the process. For each of the sub-models, the thesis first defines a set of requirements, then presents the solution designed to satisfy the requirements, and finally, re-examines the requirements to show how they are accounted for by the solution. The principal contribution of the thesis is a broader perspective on the KD process than what is currently the mainstream view. The augmented KD process model proposed by the thesis makes use of the established model, but expands it by gathering data management and knowledge representation, KD workflow and software architecture under a single unified model. Furthermore, the proposed model considers issues that are usually either overlooked or treated as separate from the KD process, such as the philosophical aspect of KD. The thesis also discusses a number of technical solutions to individual sub-problems of the KD process, including two software frameworks and four case-study applications that serve as concrete implementations and illustrations of several key features of the proposed process modelTiivistelmä Tiedonlouhintateknologialla etsitään automoidusti tietoa suurista määristä digitaalista dataa. Vakiintunut prosessimalli kuvaa tiedonlouhintaprosessia lineaarisesti ja teknologiakeskeisesti sarjana muunnoksia, jotka jalostavat raakadataa yhä abstraktimpiin ja tiivistetympiin esitysmuotoihin. Todellisissa tiedonlouhintaprosesseissa on kuitenkin aina osa-alueita, joita tällainen malli ei kata riittävän hyvin. Erityisesti on huomattava, että eräät prosessin tärkeimmistä toimijoista ovat ihmisiä, eivät teknologiaa, ja että heidän toimintansa prosessissa on luonteeltaan vuorovaikutteista eikä sarjallista. Tässä väitöskirjassa ehdotetaan vakiintuneen mallin täydentämistä siten, että tämä tiedonlouhintaprosessin laiminlyöty ulottuvuus otetaan huomioon. Ehdotettu prosessimalli koostuu kolmesta osamallista, jotka ovat tietomalli, työnkulkumalli ja arkkitehtuurimalli. Kukin osamalli tarkastelee tiedonlouhintaprosessia eri näkökulmasta: tietomallin näkökulma käsittää tiedon eri olomuodot sekä muunnokset olomuotojen välillä, työnkulkumalli kuvaa prosessin toimijat sekä niiden väliset vuorovaikutukset, ja arkkitehtuurimalli ohjaa prosessin suorittamista tukevien ohjelmistojen suunnittelua. Väitöskirjassa määritellään aluksi kullekin osamallille joukko vaatimuksia, minkä jälkeen esitetään vaatimusten täyttämiseksi suunniteltu ratkaisu. Lopuksi palataan tarkastelemaan vaatimuksia ja osoitetaan, kuinka ne on otettu ratkaisussa huomioon. Väitöskirjan pääasiallinen kontribuutio on se, että se avaa tiedonlouhintaprosessiin valtavirran käsityksiä laajemman tarkastelukulman. Väitöskirjan sisältämä täydennetty prosessimalli hyödyntää vakiintunutta mallia, mutta laajentaa sitä kokoamalla tiedonhallinnan ja tietämyksen esittämisen, tiedon louhinnan työnkulun sekä ohjelmistoarkkitehtuurin osatekijöiksi yhdistettyyn malliin. Lisäksi malli kattaa aiheita, joita tavallisesti ei oteta huomioon tai joiden ei katsota kuuluvan osaksi tiedonlouhintaprosessia; tällaisia ovat esimerkiksi tiedon louhintaan liittyvät filosofiset kysymykset. Väitöskirjassa käsitellään myös kahta ohjelmistokehystä ja neljää tapaustutkimuksena esiteltävää sovellusta, jotka edustavat teknisiä ratkaisuja eräisiin yksittäisiin tiedonlouhintaprosessin osaongelmiin. Kehykset ja sovellukset toteuttavat ja havainnollistavat useita ehdotetun prosessimallin merkittävimpiä ominaisuuksia
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Ewert, Kevin. "An Adaptive Machine Learning Approach to Knowledge Discovery in Large Datasets." NSUWorks, 2006. http://nsuworks.nova.edu/gscis_etd/510.
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Large text databases, such as medical records, on-line journals, or the Internet, potentially contain a great wealth of data and knowledge. However, text representation of factual information and knowledge is difficult to process. Analyzing these large text databases often rely upon time consuming human resources for data mining. Since a textual format is a very flexible way to describe and store various types of information, large amounts of information are often retained and distributed as text. 'The amount of accessible textual data has been increasing rapidly. Such data may potentially contain a great wealth of knowledge. However, analyzing huge amounts of textual data requires a tremendous amount of work in reading al l of the text and organizing the content. Thus, the increase in accessible textual data has caused an information flood in spite of hope of becoming knowledgeable about various topics" (Nasukawa and Nagano, 2001).
Preliminary research focused on key concepts and techniques derived from clustering methodology, machine learning, and other communities within the arena of data mining. The research was based on a two-stage machine-intelligence system that clustered and filtered large datasets. The overall objective was to optimize response time through parallel processing while attempting to reduce potential errors due to knowledge manipulation. The results generated by the two-stage system were reviewed by domain experts and tested using traditional methods that included multi variable regression analysis and logic testing for accuracy. The two-stage prototype developed a model that was 85 to 90% accurate in determining childhood asthma and disproved existing stereotypes related to sleep breathing disorders. Detail results will be discussed in the proposed dissertation.
While the initial research demonstrated positive results in processing large text datasets limitations were identified. These limitations included processing de lays resulting from equal distribution of processing in a heterogeneous client environment and utilizing the results derived from the second-stage as inputs for the first-stage. To address these limitations the proposed doctoral research will investigate the dynamic distribution of processing in heterogeneous environment and cyclical learning involving the second stage neural network clients modifying the first-stage expert systems.
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Carbonara, Leonardo. "Improving the effectiveness and the efficiency of Knowledge Base Refinement." Thesis, University of Aberdeen, 1996. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU602039.
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Knowledge Base Refinement is an area of Machine Learning whose primary goal is the automatic detection and correction of errors in faulty expert system's knowledge bases. A very important feature of a refinement system is the mechanism used to select the refinements to be implemented. Since there are usually different ways to fix a fault, most current Knowledge Base Refinement systems use extensive heuristics to choose one or a few alternative refinements from a set of possible corrections. This approach is justified by the intention of avoiding the computational problems inherent in the generation and testing of multiple refinements. On the other hand, such systems are liable to miss solutions. The opposite approach was adopted by the Knowledge Base Refinement system KRUST which proposed many alternative corrections to refine each wrongly-solved example. Although KRUST demonstrated the feasibility of this approach, the potential of multiple refinement generation could not be fully exploited since the system used a limited set of refinement operators in order to contain the number of alternative fixes generated for each fault, and hence was unable to rectify certain kinds of errors. Additionally, the time taken to produce and test a set of refined knowledge bases was considerable for any non-trivial knowledge base. This thesis presents a major revision of the KRUST system. Like its predecessor, the resulting system, STALKER, proposes many alternative refinements to correct each wrongly classified example in the training set. Two enhancements have been made: the class of errors handled by KRUST has been augmented through the introduction of inductive refinement operators; the testing phase of Knowledge Base Refinement has been speeded up considerably by means of a technique based on a Truth Maintenance System (TMS). The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time, STALKER is very efficient since KRUST's computationally expensive implementation and testing of refined knowledge bases has been replaced by a TMS-based simulator.
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Bhandari, Archna. "Enhancements to the frame virtual machine /." Online version of thesis, 1989. http://hdl.handle.net/1850/10581.
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Hutchinson, Ben. "The automatic acquisition of knowledge about discourse connectives." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/852.
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This thesis considers the automatic acquisition of knowledge about discourse connectives. It focuses in particular on their semantic properties, and on the relationships that hold between them. There is a considerable body of theoretical and empirical work on discourse connectives. For example, Knott (1996) motivates a taxonomy of discourse connectives based on relationships between them, such as HYPONYMY and EXCLUSIVE, which are defined in terms of substitution tests. Such work requires either great theoretical insight or manual analysis of large quantities of data. As a result, to date no manual classification of English discourse connectives has achieved complete coverage. For example, Knott gives relationships between only about 18% of pairs obtained from a list of 350 discourse connectives. This thesis explores the possibility of classifying discourse connectives automatically, based on their distributions in texts. This thesis demonstrates that state-of-the-art techniques in lexical acquisition can successfully be applied to acquiring information about discourse connectives. Central to this thesis is the hypothesis that distributional similarity correlates positively with semantic similarity. Support for this hypothesis has previously been found for word classes such as nouns and verbs (Miller and Charles, 1991; Resnik and Diab, 2000, for example), but there has been little exploration of the degree to which it also holds for discourse connectives. We investigate the hypothesis through a number of machine learning experiments. These experiments all use unsupervised learning techniques, in the sense that they do not require any manually annotated data, although they do make use of an automatic parser. First, we show that a range of semantic properties of discourse connectives, such as polarity and veridicality (whether or not the semantics of a connective involves some underlying negation, and whether the connective implies the truth of its arguments, respectively), can be acquired automatically with a high degree of accuracy. Second, we consider the tasks of predicting the similarity and substitutability of pairs of discourse connectives. To assist in this, we introduce a novel information theoretic function based on variance that, in combination with distributional similarity, is useful for learning such relationships. Third, we attempt to automatically construct taxonomies of discourse connectives capturing substitutability relationships. We introduce a probability model of taxonomies, and show that this can improve accuracy on learning substitutability relationships. Finally, we develop an algorithm for automatically constructing or extending such taxonomies which uses beam search to help find the optimal taxonomy.
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Pietersma, Diederik. "Machine-learning assisted development of a knowledge-based system in dairy farming." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38257.
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The goal of this research was to explore the use of machine learning to assist in the development of knowledge-based systems (KBS) in dairy farming. A framework was first developed which described the various types of management and control activities in dairy farming and the types of information flows among these activities. This framework provided a basis for the creation of computerized information systems and helped to identify the analysis of group-average lactation curves as a promising area of application. A case-acquisition and decision-support system was developed to assist a domain specialist in generating example cases for machine learning. The specialist classified data from 33 herds enrolled with the Quebec dairy herd analysis service, resulting in 1428 lactations and 7684 tests of individual cows, classified as outlier or non-outlier, and 99 interpretations of group-average lactation curves. To enable the performance analysis of classifiers, generated with machine learning from these small data sets, a method was established involving cross-validation runs, relative operating characteristic curves, and analysis of variance. In experiments to filter lactations and tests, classification performance was significantly affected by preprocessing of examples, creation of additional attributes, choice of machine-learning algorithm, and algorithm configuration. For the filtering of individual tests, naive-Bayes classification showed significantly better performance than decision-tree induction. However, the specialist considered the decision trees as more transparent than the knowledge generated with naive Bayes. The creation of a series of three classifiers with increased sensitivity at the expense of reduced specificity per classification task, allows users of a final KBS to choose the desired tendency of classifying new cases as abnormal. For the main interpretation tasks, satisfactory performance was achieved. For the filtering tasks, performance was fai
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Todd, Martin. "Combining knowledge based systems and machine learning for turbine generator condition monitoring." Thesis, University of Strathclyde, 2009. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17839.
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Gheyas, Iffat A. "Novel computationally intelligent machine learning algorithms for data mining and knowledge discovery." Thesis, University of Stirling, 2009. http://hdl.handle.net/1893/2152.
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This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model.
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Lucas, Yvan. "Credit card fraud detection using machine learning with integration of contextual knowledge." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI110.
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La détection de fraude par carte de crédit présente plusieurs caractéristiques qui en font une tâche difficile. Tout d'abord, les attributs décrivant une transaction ignorent les informations séquentielles. Deuxièmement, les comportements d'achat et les stratégies de fraude peuvent changer au fil du temps, rendant progressivement une fonction de décision apprise par un classifieur non pertinente. Nous avons effectué une analyse exploratoire afin de quantifier le dataset shift jour par jour et avons identifé des périodes calendaires qui ont des propriétés différentes au sein du jeu de données. La stratégie principale pour intégrer des informations séquentielles consiste à créer un ensemble d'attributs qui sont des statistiques descriptives obtenues en agrégeant les séquences de transactions des titulaires de carte. Nous avons utilisé cette méthode comme méthode de référence pour la détection des fraudes à la carte de crédit. Nous avons proposé une stratégie pour la création d'attributs basés sur des modèles de Markov cachés (HMM) caractérisant la transaction par différents points de vue afin d'intégrer un large spectre d'informations séquentielles au sein des transactions. En fait, nous modélisons les comportements authentiques et frauduleux des commerçants et des détenteurs de cartes selon deux caractéristiques univariées: la date et le montant des transactions. Notre approche à perspectives multiples basée sur des HMM permet un prétraitement automatisé des données pour modéliser les corrélations temporelles. Des expériences menées sur un vaste ensemble de données de transactions de cartes de crédit issu du monde réel (46 millions de transactions effectuées par des porteurs de carte belges entre mars et mai 2015) ont montré que la stratégie proposée pour le prétraitement des données basé sur les HMM permet de détecter davantage de transactions frauduleuses quand elle est combinée à la stratégie de prétraitement des données par aggrégationsThe detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy
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Schra-Martin, Nicole. "Improving Machine Changeover/Setup Times by Increasing U.S. Manufacturers' Knowledge of 5S." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2883.
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The 5S process is one of the techniques born out of Japanese manufacturing. Ohno, the developer of 5S, found that when manufacturing waste is eliminated, costs are reduced and profits increase. This is the bases of 5S and this research. The cost of U.S. manufactured products is higher compared to the cost of products from other global manufacturers that use 5S. This study was conducted to determine if implementing 5S in U.S. manufacturing could change U.S. manufacturing cost and if using 5S could impact U.S. manufacturing. The research questions focused on the relationship between 5S and changeover/setup times on production machines. The method was quantitative utilizing a quasi-experimental pretest/posttest design. Three manufacturing companies in Oregon made up the sample. A baseline 5S scorecard was completed recording changeover/ setup times on production machines at each of the companies. Interviews were conducted in a 30-minute training intervention on implementing 5S at each company location. Using a 5S scorecard, the waste in each company was assessed once every 2 weeks for 4 months. The number of 5S assessments varied based on the time each company location took to implement 5S. Once 5S was implemented fully, changeover/setup times for each machine were measured and analyzed using z or t statistics. Results showed a significant (p < .05) decrease to changeover/setup times at 2 companies, supporting the hypothesis that 5S could reduce cost in US manufacturing. Positive social change may be possible when showing how 5S can decrease changeover/setup times providing more production time and reducing overhead cost going into U.S. manufactured products, which in turn makes them more competitive in the global marketplace and potentially brings manufacturing jobs back to the U.S.
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Alirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
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The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name. In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase. Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities. The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set. The software that has been implemented and used in this project has been implemented in C.
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Lynch, Paul Kieran. "The generation of knowledge based systems for interactive nonlinear constrained optimisation." Thesis, Queen's University Belfast, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388221.
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Patterson, William Robert David. "Introspective techniques for maintaining retrieval knowledge in case-base reasoning." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365937.
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Xu, Jian. "Iterative Aggregation of Bayesian Networks Incorporating Prior Knowledge." Miami University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=miami1105563019.
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Shanahan, James Gerard. "Cartesian granule features : knowledge discovery for classification and prediction." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245529.
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Scaiano, Martin. "An Automatically Generated Lexical Knowledge Base with Soft Definitions." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34606.
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There is a need for methods that understand and represent the meaning of text for use in Artificial Intelligence (AI). This thesis demonstrates a method to automatically extract a lexical knowledge base from dictionaries for the purpose of improving machine reading. Machine reading refers to a process by which a computer processes natural language text into a representation that supports inference or inter-connection with existing knowledge (Clark and Harrison, 2010).1
There are a number of linguistic ideas associated with representing and applying the meaning of words which are unaddressed in current knowledge representations. This work draws heavily from the linguistic theory of frame semantics (Fillmore, 1976). A word is not a strictly defined construct; instead, it evokes our knowledge and experiences, and this information is adapted to a given context by human intelligence. This can often be seen in dictionaries, as a word may have many senses, but some are only subtle variations of the same theme or core idea. Further unaddressed issue is that sentences may have multiple reasonable and valid interpretations (or readings).
This thesis postulates that there must be algorithms that work with symbolic rep- resentations which can model how words evoke knowledge and then contextualize that knowledge. I attempt to answer this previously unaddressed question, “How can a sym- bolic representation support multiple interpretations, evoked knowledge, soft word senses, and adaptation of meaning?” Furthermore, I implement and evaluate the proposed so- lution.
This thesis proposes the use of a knowledge representation called Multiple Interpre- tation Graphs (MIGs), and a lexical knowledge structure called auto-frames to support contextualization. MIG is used to store a single auto-frame, the representation of a sen- tence, or an entire text. MIGs and auto-frames are produced from dependency parse trees using an algorithm I call connection search. MIG supports representing multiple different interpretations of a text, while auto-frames combine multiple word senses and in- formation related to the word into one representation. Connection search contextualizes MIGs and auto-frames, and reduces the number of interpretations that are considered valid.
In this thesis, as proof of concept and evaluation, I extracted auto-frames from Long- man Dictionary of Contemporary English (LDOCE). I take the point of view that a word’s meaning depends on what it is connected to in its definition. I do not use a
1The term machine reading was coined by Etzioni et al. (2006). ii

predetermined set of semantic roles; instead, auto-frames focus on the connections or mappings between a word’s context and its definitions.
Once I have extracted the auto-frames, I demonstrate how they may be contextu- alized. I then apply the lexical knowledge base to reading comprehension. The results show that this approach can produce good precision on this task, although more re- search and refinement is needed. The knowledge base and source code is made available to the community at http://martin.scaiano.com/Auto-frames.html or by contacting martin@scaiano.com.
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Sedighian, Kamran. "A user interface builder/manager for knowledge craft /." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=64008.
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Yu, Ting. "Incorporating prior domain knowledge into inductive machine learning: its implementation in contemporary capital markets." University of Technology, Sydney. Faculty of Information Technology, 2007. http://hdl.handle.net/2100/385.
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An ideal inductive machine learning algorithm produces a model best approximating an underlying target function by using reasonable computational cost. This requires the resultant model to be consistent with the training data, and generalize well over the unseen data. Regular inductive machine learning algorithms rely heavily on numerical data as well as general-purpose inductive bias. However certain environments contain rich domain knowledge prior to the learning task, but it is not easy for regular inductive learning algorithms to utilize prior domain knowledge. This thesis discusses and analyzes various methods of incorporating prior domain knowledge into inductive machine learning through three key issues: consistency, generalization and convergence. Additionally three new methods are proposed and tested over data sets collected from capital markets. These methods utilize financial knowledge collected from various sources, such as experts and research papers, to facilitate the learning process of kernel methods (emerging inductive learning algorithms). The test results are encouraging and demonstrate that prior domain knowledge is valuable to inductive learning machines.
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Mestres, Sugrañes Albert. "Knowledge-defined networking : a machine learning based approach for network and traffic modeling." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461831.
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The research community has considered in the past the application of Machine Learning (ML) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this thesis, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of ML techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and ML, and provide use-cases that illustrate its applicability and benefits. We also present some relevant use-cases, in which ML tools can be useful. We refer to this new paradigm as Knowledge-Defined Networking (KDN).
In this context, ML can be used as a network modeling technique to build models that estimate the network performance. Network modeling is a central technique to many networking functions, for instance in the field of optimization. One of the objective of this thesis is to provide an answer to the following question: Can neural networks accurately model the performance of a computer network as a function of the input traffic?. In this thesis, we focus mainly on modeling the average delay, but also on estimating the jitter and the packets lost. For this, we assume the network as a black-box that has as input a traffic matrix and as output the desired performance matrix. Then we train different regressors, including deep neural networks, and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. Moreover, we also study the impact of having multiple traffic flows between each pair of nodes.
We also explore the use of ML techniques in other network related fields. One relevant application is traffic forecasting. Accurate forecasting enables scaling up or down the resources to efficiently accommodate the load of traffic. Such models are typically based on traditional time series ARMA or ARIMA models. We propose a new methodology that results from the combination of an ARIMA model with an ANN. The Neural Network greatly improves the ARIMA estimation by modeling complex and nonlinear dependencies, particularly for outliers. In order to train the Neural Network and to improve the outliers estimation, we use external information: weather, events, holidays, etc. The main hypothesis is that network traffic depends on the behavior of the end-users, which in turn depend on external factors. We evaluate the accuracy of our methodology using real-world data from an egress Internet link of a campus network. The analysis shows that the model works remarkably well, outperforming standard ARIMA models.
Another relevant application is in the Network Function Virtualization (NFV). The NFV paradigm makes networks more flexible by using Virtual Network Functions (VNF) instead of dedicated hardware. The main advantage is the flexibility offered by these virtual elements. However, the use of virtual nodes increases the difficulty of modeling such networks. This problem may be addressed by the use of ML techniques, to model or to control such networks. As a first step, we focus on the modeling of the performance of single VNFs as a function of the input traffic. In this thesis, we demonstrate that the CPU consumption of a VNF can be estimated only as a function of the input traffic characteristics.L'aplicació de tècniques d'aprenentatge automàtic (ML) pel control i operació de xarxes informàtiques ja s'ha plantejat anteriorment per la comunitat científica. Un exemple important és "Knowledge Plane", proposat per D. Clark et al. Tot i això, aquestes propostes no s'han utilitzat ni implementat mai en aquest camp. En aquesta tesi, explorem els motius que han fet impossible l'adopció fins al present, i que ara en permeten la implementació. El principal motiu és l'adopció de dos nous paradigmes: Software-Defined Networking (SDN) i Network Analytics (NA), que permeten la utilització de tècniques d'aprenentatge automàtic en el context de control i operació de xarxes informàtiques. En aquesta tesi, es descriu aquest paradigma, que aprofita les possibilitats ofertes per SDN, per NA i per ML, i s'expliquen aplicacions en el món de la informàtica i les comunicacions on l'aplicació d'aquestes tècniques poden ser molt beneficioses. Hem anomenat a aquest paradigma Knowledge-Defined Networking (KDN). En aquest context, una de les aplicacions de ML és el modelatge de xarxes informàtiques per estimar-ne el comportament. El modelatge de xarxes és un camp de recerca important el aquest camp, i que permet, per exemple, optimitzar-ne el seu rendiment. Un dels objectius de la tesi és respondre la següent pregunta: Pot una xarxa neuronal modelar de manera acurada el comportament d'una xarxa informàtica en funció del tràfic d'entrada? Aquesta tesi es centra principalment en el modelatge del retard mig (temps entre que s'envia i es rep un paquet). També s'estudia com varia aquest retard (jitter) i el nombre de paquets perduts. Per fer-ho, s'assumeix que la xarxa és totalment desconeguda i que només es coneix la matriu de tràfic d'entrada i la matriu de rendiment com a sortida. Es fan servir diferents tècniques de ML, com ara regressors lineals i xarxes neuronals, i se n'avalua la precisió per diferents xarxes i diferents configuracions de xarxa i tràfic. Finalment, també s'estudia l'impacte de tenir múltiples fluxos entre els parells de nodes. En la tesi, també s'explora l'ús de tècniques d¿aprenentatge automàtic en altres camps relacionats amb les xarxes informàtiques. Un cas rellevant és la predicció de tràfic. Una bona estimació del tràfic permet preveure la utilització dels diversos elements de la xarxa i optimitzar-ne el seu rendiment. Les tècniques tradicionals de predicció de tràfic es basen en tècniques de sèries temporals, com ara models ARMA o ARIMA. En aquesta tesis es proposa una nova metodologia que combina un model ARIMA amb una xarxa neuronal. La xarxa neuronal millora la predicció dels valors atípics, que tenen comportament complexos i no lineals. Per fer-ho, s'incorpora a l'anàlisi l'ús d'informació externa, com ara: informació meteorològica, esdeveniments, vacances, etc. La hipòtesi principal és que el tràfic de xarxes informàtiques depèn del comportament dels usuaris finals, que a la vegada depèn de factors externs. Per això, s'avalua la precisió de la metodologia presentada fent servir dades reals d'un enllaç de sortida de la xarxa d'un campus. S'observa que el model presentat funciona bé, superant la precisió de models ARIMA estàndards. Una altra aplicació important és en el camp de Network Function Virtualization (NFV). El paradigma de NFV fa les xarxes més flexibles gràcies a l'ús de Virtual Network Functions (VNF) en lloc de dispositius específics. L'avantatge principal és la flexibilitat que ofereixen aquests elements virtuals. Per contra, l'ús de nodes virtuals augmenta la dificultat de modelar aquestes xarxes. Aquest problema es pot estudiar també mitjançant tècniques d'aprenentatge automàtic, tant per modelar com per controlar la xarxa. Com a primer pas, aquesta tesi es centra en el modelatge del comportament de VNFs treballant soles en funció del tràfic que processen. Concretament, es demostra que el consum de CPU d'una VNF es pot estimar a partir a partir de diverses característiques del tràfic d'entrada.
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Gooch, Richard M. "Machine learning techniques for signal processing, pattern recognition and knowledge extraction from examples." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294898.
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Annane, Amina. "Using Background Knowledge to Enhance Biomedical Ontology Matching." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS032/document.
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Les sciences de la vie produisent de grandes masses de données (par exemple, des essais cliniques et des articles scientifiques). L'intégration et l'analyse des différentes bases de données liées à la même question de recherche, par exemple la corrélation entre phénotypes et génotypes, sont essentielles pour découvrir de nouvelles connaissances. Pour cela, la communauté des sciences de la vie a adopté les techniques du Web sémantique pour réaliser l'intégration et l'interopérabilité des données, en particulier les ontologies. En effet, les ontologies représentent la brique de base pour représenter et partager la quantité croissante de données sur le Web. Elles fournissent un vocabulaire commun pour les humains, et des définitions d'entités formelles pour les machines.Un grand nombre d'ontologies et de terminologies biomédicales a été développé pour représenter et annoter les différentes bases de données existantes. Cependant, celles qui sont représentées avec différentes ontologies qui se chevauchent, c'est à dire qui ont des parties communes, ne sont pas interopérables. Il est donc crucial d'établir des correspondances entre les différentes ontologies utilisées, ce qui est un domaine de recherche actif connu sous le nom d'alignement d'ontologies.Les premières méthodes d'alignement d'ontologies exploitaient principalement le contenu lexical et structurel des ontologies à aligner. Ces méthodes sont moins efficaces lorsque les ontologies à aligner sont fortement hétérogènes lexicalement, c'est à dire lorsque des concepts équivalents sont décrits avec des labels différents. Pour pallier à ce problème, la communauté d'alignement d'ontologies s'est tournée vers l'utilisation de ressources de connaissance externes en tant que pont sémantique entre les ontologies à aligner. Cette approche soulève plusieurs nouvelles questions de recherche, notamment : (1) la sélection des ressources de connaissance à utiliser, (2) l'exploitation des ressources sélectionnées pour améliorer le résultat d'alignement. Plusieurs travaux de recherche ont traité ces problèmes conjointement ou séparément. Dans notre thèse, nous avons fait une revue systématique et une comparaison des méthodes proposées dans la littérature. Puis, nous nous sommes intéressés aux deux questions.Les ontologies, autres que celles à aligner, sont les ressources de connaissance externes (Background Knowledge : BK) les plus utilisées. Les travaux apparentés sélectionnent souvent un ensemble d'ontologies complètes en tant que BK même si, seuls des fragments des ontologies sélectionnées sont réellement efficaces pour découvrir de nouvelles correspondances. Nous proposons une nouvelle approche qui sélectionne et construit une ressource de connaissance à partir d'un ensemble d'ontologies. La ressource construite, d'une taille réduite, améliore, comme nous le démontrons, l'efficience et l'efficacité du processus d'alignement basé sur l'exploitation de BK.L'exploitation de BK dans l'alignement d'ontologies est une épée à double tranchant : bien qu'elle puisse augmenter le rappel (i.e., aider à trouver plus de correspondances correctes), elle peut réduire la précision (i.e., générer plus de correspondances incorrectes). Afin de faire face à ce problème, nous proposons deux méthodes pour sélectionner les correspondances les plus pertinentes parmi les candidates qui se basent sur : (1) un ensemble de règles et (2) l'apprentissage automatique supervisé. Nous avons expérimenté et évalué notre approche dans le domaine biomédical, grâce à la profusion de ressources de connaissances en biomédecine (ontologies, terminologies et alignements existants). Nous avons effectué des expériences intensives sur deux benchmarks de référence de la campagne d'évaluation de l'alignement d'ontologie (OAEI). Nos résultats confirment l'efficacité et l'efficience de notre approche et dépassent ou rivalisent avec les meilleurs résultats obtenusLife sciences produce a huge amount of data (e.g., clinical trials, scientific articles) so that integrating and analyzing all the datasets related to a given research question like the correlation between phenotypes and genotypes, is a key element for knowledge discovery. The life sciences community adopted Semantic Web technologies to achieve data integration and interoperability, especially ontologies which are the key technology to represent and share the increasing amount of data on the Web. Indeed, ontologies provide a common domain vocabulary for humans, and formal entity definitions for machines.A large number of biomedical ontologies and terminologies has been developed to represent and annotate various datasets. However, datasets represented with different overlapping ontologies are not interoperable. It is therefore crucial to establish correspondences between the ontologies used; an active area of research known as ontology matching.Original ontology matching methods usually exploit the lexical and structural content of the ontologies to align. These methods are less effective when the ontologies to align are lexically heterogeneous i.e., when equivalent concepts are described with different labels. To overcome this issue, the ontology matching community has turned to the use of external knowledge resources as a semantic bridge between the ontologies to align. This approach arises several new issues mainly: (1) the selection of these background resources, (2) the exploitation of the selected resources to enhance the matching results. Several works have dealt with these issues jointly or separately. In our thesis, we made a systematic review and historical evaluation comparison of state-of-the-art approaches.Ontologies, others than the ones to align, are the most used background knowledge resources. Related works often select a set of complete ontologies as background knowledge, even if, only fragments of the selected ontologies are actually effective for discovering new mappings. We propose a novel BK-based ontology matching approach that selects and builds a knowledge resource with just the right concepts chosen from a set of ontologies. The conducted experiments showed that our BK selection approach improves efficiency without loss of effectiveness.Exploiting background knowledge resources in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). We propose two methods to select the most relevant mappings from the candidate ones: (1) based on a set of rules and (2) with Supervised Machine Learning. We experiment and evaluate our approach in the biomedical domain, thanks to the profusion of knowledge resources in biomedicine (ontologies, terminologies and existing alignments).We evaluated our approach with extensive experiments on two Ontology Alignment Evaluation Initiative (OAEI) benchmarks. Our results confirm the effectiveness and efficiency of our approach and overcome or compete with state-of-the-art matchers exploiting background knowledge resources
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Johansson, Josefin. "The knowledge base of machine learning, across data analytics teams in a matrix organization. : An exploratory case study on machine learning." Thesis, Karlstads universitet, Handelshögskolan, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-66283.
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Machine learning is a field within the broader concept of artificial intelligence and addresses the questions of how to build systems which learn from experience. The field is one of the oldest disciplines in computer science but has had many recent advancements due to the large amounts of data being generated. Today, machine learning together with artificial intelligence is seen as the two most rapidly growing fields within computer science. The purpose of this thesis is to explore and identify the current knowledge base of machine learning across data analytics teams, within the matrix organization Wise Inc.. This study has been performed using an exploratory case study method, based on the embedded units within the matrix organization. In this research, the units represent thirteen different cross-functional teams existing within the Wise Inc. organization. All thirteen teams are data analytics teams and performing a variety of different analytics depending on the team’s individual purpose. The analysis of embedded units has been performed within the units, but also across units. Using data collected through a qualitative questionnaire and interview, the knowledge base of machine learning could be explored and identified. Analysing the collected data, it was showed that the knowledge base across the data analytics teams in Wise Inc. is currently relatively low. Two key teams have been identified to have a very high level of knowledge. The knowledge base was examined based on participants theoretical and practical knowledge when it comes to machine learning. The aspect of machine learning usage and experience was included in the analysis and appeared to show a weak positive correlation to the overall knowledge. However, the statistical significance could not be determined. The empirical study also indicates that across teams, the level of knowledge is slightly higher than the level of experience. As a positive result, most participants appear to have a good theoretical understanding of machine learning in relation to artificial intelligence, which normally is one of the most common miss-interpretations. Even though the overall knowledge base is low, there are a few key people which stand out with a high knowledge base amongst teams. Observing the team as a whole the knowledge base is medium, but when looking at the individuals within the team there are a few key members with high expertise. These people are not working within the two teams identified with a high machine learning knowledge base but are part of other analytics teams. These people are important to identify as they can contribute with great value to the Wise Inc. organization.
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Kaithi, Bhargavacharan Reddy. "Knowledge Graph Reasoning over Unseen RDF Data." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1571955816559707.
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