Relevant bibliographies by topics / Classifier paradigms

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Classifier paradigms'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Classifier paradigms"

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Zhao, Xianfeng, Jie Zhu, and Haibo Yu. "On More Paradigms of Steganalysis." International Journal of Digital Crime and Forensics 8, no. 2 (April 2016): 1–15. http://dx.doi.org/10.4018/ijdcf.2016040101.

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Up to now, most researches on steganalysis concentrate on one extreme case. Typically, the a priori knowledge of the embedding way and cover-media is assumed known in the classifier training and even feature design stage. However, the steganalysis in the real world is done with different levels of such knowledge so that there can be various paradigms for doing it. Although some researchers have addressed the situations, there is still a lack of a systematic approach to defining the various paradigms. In this paper, the authors give such an approach by first defining four extreme paradigms, and then defining the rest among them. Each paradigm is related with two sets of assumed known a priori knowledge respectively about the steganographic algorithm and cover-media, and each paradigm corresponds to a particular case of steganalysis. Also we will see that different paradigms can have very different aims so that the designs may be various.
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Martišius, Ignas, and Robertas Damaševičius. "A Prototype SSVEP Based Real Time BCI Gaming System." Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/3861425.

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Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.
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Xu, Minpeng, Jing Liu, Long Chen, Hongzhi Qi, Feng He, Peng Zhou, Baikun Wan, and Dong Ming. "Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers." International Journal of Neural Systems 26, no. 03 (April 7, 2016): 1650010. http://dx.doi.org/10.1142/s0129065716500106.

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Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain–computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject’s data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra- and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject’s data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms.
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Govindarajan, M., and RM Chandrasekaran. "A Hybrid Multilayer Perceptron Neural Network for Direct Marketing." International Journal of Knowledge-Based Organizations 2, no. 3 (July 2012): 63–73. http://dx.doi.org/10.4018/ijkbo.2012070104.

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Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
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Yenkikar, Anuradha, C. Narendra Babu, and D. Jude Hemanth. "Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble." PeerJ Computer Science 8 (September 20, 2022): e1100. http://dx.doi.org/10.7717/peerj-cs.1100.

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The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.
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Et. al., G. Stalin Babu,. "Exploiting of Classification Paradigms for Early diagnosis of Alzheimer’s disease." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 25, 2021): 281–88. http://dx.doi.org/10.17762/itii.v9i2.345.

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Alzheimer’s disorder is an incurable neurodegenerative disease that ordinarily affects the aged population. Coherent automated assessment methods are essential for Alzheimer's disease diagnosis in early from distinct images modalities using Machine Learning. This article focuses on exploring various feature extraction and classification methods for early detection of AD proposed by researchers and proposes a modern predictive model that includes Voxel based Texture analysis of brain images for extract features and Optimized Classifier Deep Convolution Neural Network (DCNN) employed for enhance accuracy.
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Zhang, Yang, and Peter I. Rockett. "A Generic Multi-dimensional Feature Extraction Method Using Multiobjective Genetic Programming." Evolutionary Computation 17, no. 1 (March 2009): 89–115. http://dx.doi.org/10.1162/evco.2009.17.1.89.

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In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.
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Fisch, Dominik, Bernhard Kühbeck, Bernhard Sick, and Seppo J. Ovaska. "So near and yet so far: New insight into properties of some well-known classifier paradigms." Information Sciences 180, no. 18 (September 2010): 3381–401. http://dx.doi.org/10.1016/j.ins.2010.05.030.

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Stojic, Filip, and Tom Chau. "Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control." International Journal of Neural Systems 30, no. 06 (May 27, 2020): 2050026. http://dx.doi.org/10.1142/s0129065720500264.

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Brain–computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.
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Pramukantoro, Eko Sakti, and Akio Gofuku. "A Heartbeat Classifier for Continuous Prediction Using a Wearable Device." Sensors 22, no. 14 (July 6, 2022): 5080. http://dx.doi.org/10.3390/s22145080.

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Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
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Dissertations / Theses on the topic "Classifier paradigms"

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Taheri, Sona. "Learning Bayesian networks based on optimization approaches." Thesis, University of Ballarat, 2012. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/36051.

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Learning accurate classifiers from preclassified data is a very active research topic in machine learning and artifcial intelligence. There are numerous classifier paradigms, among which Bayesian Networks are very effective and well known in domains with uncertainty. Bayesian Networks are widely used representation frameworks for reasoning with probabilistic information. These models use graphs to capture dependence and independence relationships between feature variables, allowing a concise representation of the knowledge as well as efficient graph based query processing algorithms. This representation is defined by two components: structure learning and parameter learning. The structure of this model represents a directed acyclic graph. The nodes in the graph correspond to the feature variables in the domain, and the arcs (edges) show the causal relationships between feature variables. A directed edge relates the variables so that the variable corresponding to the terminal node (child) will be conditioned on the variable corresponding to the initial node (parent). The parameter learning represents probabilities and conditional probabilities based on prior information or past experience. The set of probabilities are represented in the conditional probability table. Once the network structure is constructed, the probabilistic inferences are readily calculated, and can be performed to predict the outcome of some variables based on the observations of others. However, the problem of structure learning is a complex problem since the number of candidate structures grows exponentially when the number of feature variables increases. This thesis is devoted to the development of learning structures and parameters in Bayesian Networks. Different models based on optimization techniques are introduced to construct an optimal structure of a Bayesian Network. These models also consider the improvement of the Naive Bayes' structure by developing new algorithms to alleviate the independence assumptions. We present various models to learn parameters of Bayesian Networks; in particular we propose optimization models for the Naive Bayes and the Tree Augmented Naive Bayes by considering different objective functions. To solve corresponding optimization problems in Bayesian Networks, we develop new optimization algorithms. Local optimization methods are introduced based on the combination of the gradient and Newton methods. It is proved that the proposed methods are globally convergent and have superlinear convergence rates. As a global search we use the global optimization method, AGOP, implemented in the open software library GANSO. We apply the proposed local methods in the combination with AGOP. Therefore, the main contributions of this thesis include (a) new algorithms for learning an optimal structure of a Bayesian Network; (b) new models for learning the parameters of Bayesian Networks with the given structures; and finally (c) new optimization algorithms for optimizing the proposed models in (a) and (b). To validate the proposed methods, we conduct experiments across a number of real world problems. Print version is available at: http://library.federation.edu.au/record=b1804607~S4
Doctor of Philosophy
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Mawila, Ntombhimuni. "Natural language processing for researchh philosophies and paradigms dissertation (DFIT91)." Diss., 2021. http://hdl.handle.net/10500/27471.

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Research philosophies and paradigms (RPPs) reveal researchers’ assumptions and provide a systematic way in which research can be carried out effectively and appropriately. Different studies highlight cognitive and comprehension challenges of RPPs concepts at the postgraduate level. This study develops a natural language processing (NLP) supervised classification application that guides students in identifying RPPs applicable to their study. By using algorithms rooted in a quantitative research approach, this study builds a corpus represented using the Bag of Words model to train the naïve Bayes, Logistic Regression, and Support Vector Machine algorithms. Computer experiments conducted to evaluate the performance of the algorithms reveal that the Naïve Bayes algorithm presents the highest accuracy and precision levels. In practice, user testing results show the varying impact of knowledge, performance, and effort expectancy. The findings contribute to the minimization of issues postgraduates encounter in identifying research philosophies and the underlying paradigms for their studies.
Science and Technology Education
MTech. (Information Technology)
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(6997520), Bo Zhang. "A DESIGN PARADIGM FOR DC GENERATION SYSTEM." Thesis, 2020.

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The design of a dc generation system is posed as a multi-objective optimization problem which simultaneously designs the generator and the power converter. The proposed design methodology captures the interaction between various system component models and utilizes the system steady state analysis, stability analysis, and disturbance rejection analysis. System mass and power loss are considered as the optimization metrics and minimized. The methodology is demonstrated through the design of a notional dc generation system which contains a Permanent Magnet Synchronous Machine (PMSM), passive rectifier, and a dc-dc converter. To this end, a high fidelity PMSM model, passive rectifier model, semiconductor model and passive component model are developed. The output of optimization is a set of designs forming a Pareto-optimal front. Based on the requirements and the application, a design can be chosen from this set of designs. The methodology is applied to SiC based dc generation system and Si based dc generation system to quantify the advantage of Wide Bandgap (WBG) devices. A prototype SiC based dc generation system is constructed and tested at steady state. Finally a thermal equivalent circuit (TEC) based PMSM thermal model is included in the design paradigm to quantify the impact of the PMSM’s thermal performance to the system design.
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(7022165), Raj Sahu. "Design Paradigm for Modular Multilevel Converter Based Generator Rectifier Systems." Thesis, 2019.

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Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC generation systems. Integrated system design optimization of the generator-MMC system through multi-objective optimization is of interest, because such an approach allows the trade-off between competing objectives (for example, mass and loss) to be explicitly and quantitatively identified. In this work, such an optimization based design paradigm for MMC based generator rectifier systems is developed. To formulate the design problem as a multi-objective optimization problem, it is required that the system waveforms can be obtained to facilitate the imposition of constraints and the estimation of power losses. Similarly, it is also desired to include detailed electric machine magnetic and electrical analysis in design optimization, as well as aspects such as the inductor and heat sink design. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, heat sink metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. Using the developed component simulation and design models, a multi-objective optimization based design of an MMC based generator-rectifier system is conducted.
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(9874109), G. Arnold. "An exploration of the question "What is wisdom?": With particular reference to aspiration in teaching: a practical-philosophy paradigm." Thesis, 2006. https://figshare.com/articles/thesis/An_exploration_of_the_question_What_is_wisdom_With_particular_reference_to_aspiration_in_teaching_a_practical-philosophy_paradigm/13422689.

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Investigates one teacher's reflections in order to "ascertain possibilities for informing the question of what it means to 'be wise' or 'act wisely' in an educational setting". The study involves 'hermeneutic inteviews' and the philosophical approach of the research is articulated as a 'Practical-Philosophy Paradigm'.. In the progress of human lives, matters of choice and action are negotiated on a daily basis. The thesis takes this observation as its point of departure for a philosophical exploration of the question 'What is wisdom?'. After consideration of particular challenges that can be associated with wisdom inquiry, the contextualisation of the wisdom question as a practical concern is further developed by attending to the field of education. Here the wisdom question is investigated in a context where teaching is considered as an aspirational endeavour. The so-called problem of 'the gap' between aspiration and experienced reality in the practice of teaching is presented as a more specific context or point of reference for the exploration. A study of one teacher's reflections is conducted in order to ascertain possibilities for informing the question of what it means to 'be wise' or 'act wisely' in an educational setting. The study involves 'hernleneutic interviews' and the philosophical approach of the research is articulated as a 'Practical-Philosophy Paradigm' . It is proposed that the wisdom question can be usefully explored by developing a 'conversation' which draws upon two primary sources in this process of contextualisation. These two sources consist of the theoretical-philosophical perspectives contained in the literature and the 'voice' of the teacher with specific or extensive experience of the relevant concerns. The thesis accordingly aims to contribute a formulation, or way of considering the wisdom question more carefully, based on the transdisciplinary implications of the theoretical perspectives and the richness of practitioner-derived knowledge. This formulation, introduced n the opening chapter, proposes that wisdom is the balanced integration or 'nexus' of two contexts: a Context of Profundity where hermeneutical reflection is drawn towards the deeper meanings of human experience, and a Context of Practicality where wisdom is associated with the resolution of problems or the progress of human lives. The themes of profundity and practicality are found to be useful in the analyses of both the wisdom-related material in the literature and the teacher's articulation of his approach. In general, the wisdom question is encountered in the exploration as a challenge to learn a dynamic, creative and reflexive approach. For both the teacher and the researcher, the challenge is understood to be implicated in the deeper questions of the meaning of lived experience and in the practical concerns that accompany such experience.
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(6632282), Allison C. Hopkins. "Measuring the Effect of Task-Irrelevant Visuals in Augmented Reality." Thesis, 2019.

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Augmented reality (AR) allows people to view digital information overlaid on to real-world objects. While the technology is still new, it is currently being used in places such as the military and industrial assembly operations in the form of ocular devices worn on the head over the eyes. Head-mounted displays (HMDs) let people always see AR information in their field of view no matter where their head is positioned. Studies have shown that HMDs displaying information directly related to the immediate task can decreased cognitive workload and increase the speed and accuracy of task performance. However, task-irrelevant information has shown to decrease performance and accuracy of the primary task and also hinder the efficiency of processing the irrelevant information. This has been investigated in industry settings but less so in an everyday consumer context. This study proposes comparing two types of visual information (text and shapes) in AR displayed on an HMD to answer the following questions: 1) when content is of importance, which visual notification (text or shapes) is processed faster while degrading the performance of the primary task the least? And 2) When presence is of importance, which visual notification (text or shapes) is processed faster while degrading the performance of the primary task the least?

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Liu, Weiwei. "Advanced topics in multi-label learning." Thesis, 2017. http://hdl.handle.net/10453/116828.

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University of Technology Sydney. Faculty of Engineering and Information Technology.
Multi-label learning, in which each instance can belong to multiple labels simultaneously, has significantly attracted the attention of researchers as a result of its wide range of applications, which range from document classification and automatic image annotation to video annotation. Many multi-label learning models have been developed to capture label dependency. Amongst them, the classifier chain (CC) model is one of the most popular methods due to its simplicity and promising experimental results. However, CC suffers from three important problems: Does the label order affect the performance of CC? Is there any globally optimal classifier chain which can achieve the optimal prediction performance for CC? If yes, how can the globally optimal classifier chain be found? It is non-trivial to answer these problems. Another important branch of methods for capturing label dependency is encoding-decoding paradigm. Based on structural SVMs, maximum margin output coding (MMOC) has become one of the most representative encoding-decoding methods and shown promising results for multi-label classification. Unfortunately, MMOC suffers from two major limitations: 1) Inconsistent performance: D. McAllester has already proved that structural SVMs fail to converge on the optimal decoder even with infinite training data. 2) Prohibitive computational cost: the training of MMOC involves a complex quadratic programming (QP) problem over the combinatorial space, and its computational cost on the data sets with many labels is prohibitive. Therefore, it is non-trivial to break the bottlenecks of MMOC, and develop efficient and consistent algorithms for solving multi-label learning tasks. The prediction of most multi-label learning methods either scales linearly with the number of labels or involves an expensive decoding process, which usually requires solving a combinatorial optimization. Such approaches become unacceptable when tackling thousands of labels, and are impractical for real-world applications, such as document annotation. It is imperative to design an efficient, yet accurate multi-label learning algorithm with the minimum number of predictions. This thesis systematically studies how to efficiently solve aforementioned issues with provable guarantee.
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Books on the topic "Classifier paradigms"

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Buffon, Marciano, and Ivan Luiz Steffens. Tributação e constituição: Por um modo de tributar hermeneuticamente adequado à principiologia constitucional brasileira. Brazil Publishing, 2020. http://dx.doi.org/10.31012/978-65-5861-066-3.

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The formal establishment of the Law and Democratic State by the 1988 Constitution introduces a paradigm shift with the commitment of a social nature to build a free, just and solidary society. In the tax field, this change suggests a targeted taxation to achieve these ends by the use of the redistributive function, with progressive taxation. However, despite the new institutional framework, the national taxation keeps regressive, and has promoted a redistribution of income in reverse. The study aims to address how taxation is being constructed and exercised, as well as its compliance with the paradigm of democratic rule of law. The analysis runs through the conceptual outlines of Law Democratic State, arising influence of the welfare state and Constitutionalism Contemporary post-war and the use of tax function in these state models. In the second phase, the study runs for the constitutional principles on tax matters, from the Hermeneutics of the Law Review, classified into two groups with a view to its relationship with the legal security and solidarity. Finally, it examines the composition of the tax burden and its discussion in the media, gathered with the possibility of greater transparency in taxation. It also analyzes the possibility of redistribution of the tax burden by applying the constitutional principles on taxes on income, wealth and consumption. The survey results indicate the need for structural modification of taxation in search of greater progressivity, given the current regressivity, which can be achieved by using the principle arsenal already provided by the Constitution.
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Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase space, whose geometric properties define the set of all trajectories through those spaces. The section on nonlinear dynamics has chapters on chaos theory, synchronization, and networks. Chaos theory provides the language and tools to understand nonlinear systems, introducing fixed points that are classified through stability analysis and nullclines that shepherd system trajectories. Synchronization and networks are central paradigms in this book because they demonstrate how collective behavior emerges from the interactions of many individual nonlinear elements. The section on complex systems contains chapters on neural dynamics, evolutionary dynamics, and economic dynamics. The final section contains chapters on metric spaces and the special and general theories of relativity. In the second edition, sections on conventional topics, like applications of Lagrangians, have been strengthened, as well as being updated to provide a modern perspective. Several of the introductory chapters have been rearranged for improved logical flow and there are expanded homework problems at the end of each chapter.
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Book chapters on the topic "Classifier paradigms"

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Kuraku, Nagendra Vara Prasad, Yigang He, and Murad Ali. "Comparative Analysis of the Fault Diagnosis in CHMLI Using k-NN Classifier Based on Different Feature Extractions." In Machine Learning Paradigms: Theory and Application, 111–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_6.

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Tellaeche, Alberto, Xavier-P. BurgosArtizzu, Gonzalo Pajares, and Angela Ribeiro. "A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms." In Advances in Soft Computing, 72–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74972-1_11.

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Liu, Yu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, and Tinne Tuytelaars. "More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning." In Computer Vision – ECCV 2020, 699–716. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58574-7_42.

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Troussas, Christos, Akrivi Krouska, and Maria Virvou. "Trends on Sentiment Analysis over Social Networks: Pre-processing Ramifications, Stand-Alone Classifiers and Ensemble Averaging." In Machine Learning Paradigms, 161–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94030-4_7.

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. "Decision Rule Classifiers for Multi-label Decision Tables." In Rough Sets and Intelligent Systems Paradigms, 191–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08729-0_18.

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Stefanowski, Jerzy. "Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data." In Emerging Paradigms in Machine Learning, 277–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28699-5_11.

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Saha, Jayasree, and Jayanta Mukherjee. "RECAL: Reuse of Established CNN Classifier Apropos Unsupervised Learning Paradigm." In Communications in Computer and Information Science, 174–84. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8697-2_16.

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Anusuya, R., and S. Krishnaveni. "Performance Evaluation of Supervised Machine Learning Classifiers for Analyzing Agricultural Big Data." In Smart Network Inspired Paradigm and Approaches in IoT Applications, 135–50. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8614-5_8.

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Cheriguene, Soraya, Nabiha Azizi, Nawel Zemmal, Nilanjan Dey, Hayet Djellali, and Nadir Farah. "Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms." In Intelligent Systems Reference Library, 289–307. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21212-8_13.

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Badr, Youakim, and Soumya Banerjee. "Developing Modified Classifier for Big Data Paradigm: An Approach Through Bio-Inspired Soft Computing." In Studies in Big Data, 109–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53474-9_5.

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Conference papers on the topic "Classifier paradigms"

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Guijarro, Maria, Gonzalo Pajares, Raquel Abreu, Luis Garmendia, and Matilde Santos. "Design of a Hybrid Classifier for Natural Textures in Images from the Bayesian and Fuzzy Paradigms." In 2007 IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/wisp.2007.4447562.

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Azmi, Haytham, and Ratshih Sayed. "FPGA-based Implementation of a Tree-based Classifier using HW-SW Co-design." In 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT). IEEE, 2019. http://dx.doi.org/10.1109/accs-peit48329.2019.9062867.

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Luo, Yijing, Bo Han, and Chen Gong. "A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/361.

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Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery'' (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods.
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Barbosa, José Matheus Lacerda, Adriano Marabuco de Albuquerque Lima, Paulo Salgado Gomes de Mattos Neto, and Adriano Lorena Inácio de Oliveira. "Hybrid Swarm Enhanced Classifier Ensembles." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18263.

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Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia artificial de abelhas (ABC), e a alternativa híbrida das anteriores usando a técnica de multi enxames dinâmicos (DM-PSO-ABC).
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Goel, Sanjay, Prabhat Hajela, Sanjay Goel, and Prabhat Hajela. "Adaptive design optimization using classifiers based machine learning paradigm." In 38th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-1572.

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Marko, K. A., L. A. Feldkamp, and G. V. Puskorius. "Automotive diagnostics using trainable classifiers: statistical testing and paradigm selection." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137540.

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Alves, Regina Reis da Costa, Frederico Caetano Jandre de Assis Tavares, José Manoel Seixas, and Anete Trajman. "Introducing lifelong machine learning in the active tuberculosis classification through chest radiographs." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-119.

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Tuberculosis (TB) is a contagious disease which is among the top 10 causes of death in the world. In order to eliminate the disease by 2050, the treatment of TB infection (TBI) is essential, which requires radiological reports to exclude active tuberculosis. The automatic X-ray classifiers used today are based on models that do not guarantee the retention of knowledge if they need to learn new tasks over time. This work proposes the introduction of the lifelong machine learning (LML) paradigm in automatic X-ray classifiers aimed at helping to diagnose active TB (ATB). Two LML algorithms, Efficient Lifelong Learning Algorithm (ELLA) and Learning without Forgetting (LwF), are applied to the TB and pneumonia classification tasks. The results show that it is possible to keep the performance in both tasks with the LML paradigm.
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Zaigham Zaheer, Muhammad, Jin-Ha Lee, Marcella Astrid, and Seung-Ik Lee. "Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01419.

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Hunt, Martin A., James S. Goddard, Jr., James A. Mullens, Regina K. Ferrell, Bobby R. Whitus, and Ariel Ben-Porath. "Paradigm for selecting the optimum classifier in semiconductor automatic defect classification applications." In Microlithography 2000, edited by Neal T. Sullivan. SPIE, 2000. http://dx.doi.org/10.1117/12.386481.

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Mirghasemi, H., M. B. Shamsollahi, and R. Fazel-Rezai. "Assessment of Preprocessing on Classifiers Used in the P300 Speller Paradigm." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259520.

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