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Dissertations / Theses on the topic 'Classifiers'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Cornforth, David. "Classifiers for machine intelligence." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359599.

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2

Bermejo, Sánchez Sergio. "Learning with nearest neighbour classifiers." Doctoral thesis, Universitat Politècnica de Catalunya, 2000. http://hdl.handle.net/10803/6323.

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Premi extraordinari ex-aequo en l'àmbit d'Electrònica i Telecomunicacions. Convocatoria 1999 - 2000<br>Nearest Neighbour (NN) classifiers are one of the most celebrated algorithms in machine learning. In recent years, interest in these methods has flourished again in several fields (including statistics, machine learning and pattern recognition) since, in spite of their simplicity, they reveal as powerful non-parametric classification systems in real-world problems. The present work is mainly devoted to the development of new learning algorithms for these classifiers and is focused on the foll

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3

Szeto, Ka-sinn Kitty, and 司徒嘉善. "The acquisition of Cantonese classifiers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31219913.

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4

Billing, Jeffrey J. (Jeffrey Joel) 1979. "Learning classifiers from medical data." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8068.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaf 32).<br>The goal of this thesis was to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an algorithm that learns decision trees from data while SVMLight learns support vector machines from the data

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5

Tsampouka, Petroula. "Perceptron-like large margin classifiers." Thesis, University of Southampton, 2007. https://eprints.soton.ac.uk/264242/.

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We address the problem of binary linear classification with emphasis on algorithms that lead to separation of the data with large margins. We motivate large margin classification from statistical learning theory and review two broad categories of large margin classifiers, namely Support Vector Machines which operate in a batch setting and Perceptron-like algorithms which operate in an incremental setting and are driven by their mistakes. We subsequently examine in detail the class of Perceptron-like large margin classifiers. The algorithms belonging to this category are further classified on t

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6

Klautau, Aldebaro. "Speech recognition using discriminative classifiers /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3091208.

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7

CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net

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8

Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.

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The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix

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9

Mak, David Lai-woon. "The acquisition to classifiers in Cantonese." Online version, 1991. http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.293547.

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10

Haddad, Nicholas K. "Performance analysis of active sonar classifiers." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1173206177.

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11

Xue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

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12

Shadafan, Raed Salem. "Sequential training of multilayer perceptron classifiers." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387686.

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13

Mak, David Lai-Woon. "The acquisition of classifiers in Cantonese." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293547.

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14

Almazeed, Ahmad H. "Improving motion segmentation with combined classifiers." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427441.

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15

Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 53).<br>The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely re

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16

VISA, SOFIA. "FUZZY CLASSIFIERS FOR IMBALANCED DATA SETS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1182226868.

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17

Lucaci, Diana. "Interpretability for Deep Learning Text Classifiers." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41564.

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The ubiquitous presence of automated decision-making systems that have a performance comparable to humans brought attention towards the necessity of interpretability for the generated predictions. Whether the goal is predicting the system’s behavior when the input changes, building user trust, or expert assistance in improving the machine learning methods, interpretability is paramount when the problem is not sufficiently validated in real applications, and when unacceptable results lead to significant consequences. While for humans, there are no standard interpretations for the decision

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18

Frankowsky, Maximilian, and Dan Ke. "Humanness and classifiers in Mandarin Chinese." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-224789.

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Mandarin Chinese numeral classifiers receive considerable at-tention in linguistic research. The status of the general classifier 个 gè re-mains unresolved. Many linguists suggest that the use of 个 gè as a noun classifier is arbitrary. This view is challenged in the current study. Relying on the CCL-Corpus of Peking University and data from Google, we investigated which nouns for living beings are most likely classified by the general clas-sifier 个 gè. The results suggest that the use of the classifier 个 gè is motivated by an anthropocentric continuum as described by Köpcke and Zubin in the 199

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19

Neumann, Julia. "Adaptive conjoint wavelet-support vector classifiers." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11675448.

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20

Adamčík, Martin. "Limit Modes of Particulate Materials Classifiers." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-367539.

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S požadavky materiálových věd na stále menší částice jsou potřebné i nové přístupy a metody jejich klasifikace. V disertační práci jsou zkoumány struktury turbulentního proudění a trajektorie částic uvnitř dynamického větrného třídiče. Zvyšující se výpočtový výkon a nové modely turbulence a přístupy modelování komplexních plně turbulentních problémů řešením Navier-Stokesových rovnic umožňují zkoumání stále menších lokálních proudových struktur a vlastností proudění s větší přesností. Částice menší než 10 mikronů jsou více ovlivnitelné a jejich klasifikace do hrubé nebo jemné frakce závisí na m

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21

Burago, Igor. "Automated Attacks on Compression-Based Classifiers." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18439.

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Methods of compression-based text classification have proven their usefulness for various applications. However, in some classification problems, such as spam filtering, a classifier confronts one or many adversaries willing to induce errors in the classifier's judgment on certain kinds of input. In this thesis, we consider the problem of finding thrifty strategies for character-based text modification that allow an adversary to revert classifier's verdict on a given family of input texts. We propose three statistical statements of the problem that can be used by an attacker to obtain transfor

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22

Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each ser

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23

Park, Changyi. "Generalization error rates for margin-based classifiers." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1124282485.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains ix, 63 p.; also includes graphics (some col.). Includes bibliographical references (p. 60-63). Available online via OhioLINK's ETD Center

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24

Alani, Shayma. "Design of intelligent ensembled classifiers combination methods." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/12793.

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Classifier ensembling research has been one of the most active areas of machine learning for a long period of time. The main aim of generating combined classifier ensembles is to improve the prediction accuracy in comparison to using an individual classifier. A combined classifiers ensemble can improve the prediction results by compensating for the individual classifier weaknesses in certain areas and benefiting from better accuracy of the other ensembles in the same area. In this thesis, different algorithms are proposed for designing classifier ensemble combiners. The existing methods such a

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25

Vrusias, Bogdan L. "Combining unsupervised classifiers : a multimodal case study." Thesis, University of Surrey, 2004. http://epubs.surrey.ac.uk/843421/.

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Neural computing systems are trained on the principle that if a network can compute then it will learn to compute. Multi-net neural computing systems are trained on the principle that if two or more networks compute, then the multi-net will learn to compute. In this thesis I will present a novel multi-net systems that can classify a set of objects using different sets of attributes of the objects: each set is used in the training of each unsupervised self-organised network and a Hebbian network simultaneously learns the association of the most highly active neurons in the constituent unsupervi

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26

Katz, Marcel [Verfasser]. "Discriminative classifiers for speaker Recognition / Marcel Katz." Saarbrücken : Südwestdeutscher Verlag für Hochschulschriften, 2009. http://www.vdm-verlag.de.

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27

Kybartas, Rimantas. "Multi-class recognition using pair-wise classifiers." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150424-92661.

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There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the seco

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28

Cao, Yilong. "A novel loss function to train classifiers." Thesis, University of Sheffield, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.632413.

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29

Day, Matthew. "Towards optimised cascade classifiers for face detection." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495869.

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30

Ngo, Nancy T. "Accessing the semantics of Japanese numeral classifiers." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1586876.

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<p> For learners of Japanese, the semantics associated with numeral classifiers are non-transparent and often a source of difficulty in language acquisition. To better understand the accessibility of the semantics governing numeral classification and the metacognitive processes involved, this study examined acquisition of Japanese numeral classifiers in second language learning. Native speakers (<i>N</i>=48) and second language learners of Japanese (<i>N</i>=41) were presented with images of 20 items and asked to provide an appropriate classifier and explain their rationale. Items consis

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31

Zafrulla, Zahoor. "Automatic recognition of American sign language classifiers." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53461.

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Automatically recognizing classifier-based grammatical structures of American Sign Language (ASL) is a challenging problem. Classifiers in ASL utilize surrogate hand shapes for people or "classes" of objects and provide information about their location, movement and appearance. In the past researchers have focused on recognition of finger spelling, isolated signs, facial expressions and interrogative words like WH-questions (e.g. Who, What, Where, and When). Challenging problems such as recognition of ASL sentences and classifier-based grammatical structures remain relatively unexplored in the

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32

Ramakrishnan, Shubha. "A system design approach to neuromorphic classifiers." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51718.

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This work considers alternative strategies to mainstream digital approaches to signal processing - namely analog and neuromorphic solutions, for increased computing efficiency. In the context of a speech recognizer application, we use low-power analog approaches for the signal conditioning and basic auditory feature extraction, while using a neuromorphic IC for building a dendritic classifier that can be used as a low-power word spotter. In doing so, this work also aspires to posit the significance of dendrites in neural computation.

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33

Semnani, Shahram. "Design and analysis of discriminant pattern classifiers." Thesis, Loughborough University, 1993. https://dspace.lboro.ac.uk/2134/14143.

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In recent years pattern recognition has evolved to a mature discipline and has been successfully applied to various problems. A fundamental part of an automatic pattern recognition system is classification, where a pattern vector is assigned to one of a finite number of classes. This thesis reports on the development and design of pattern classifier algorithms, with particular emphasis on statistical algorithms which employ discriminant functions. The first part of this research work investigates the use of linear discriminant functions as pattern classifiers. A comparison of some well known m

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34

Li, Ming. "Sequence and text classification : features and classifiers." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426966.

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35

Hooper, Richard. "Minimal kernal classifiers for pattern recognition problems." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243052.

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36

Lee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.

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37

Chungfat, Neil C. (Neil Caye) 1979. "Context-aware activity recognition using TAN classifiers." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87220.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 73-77).<br>by Neil C. Chungfat.<br>M.Eng.

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38

Wang, Lianqing. "Origin and Development of Classifiers in Chinese." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1392056967.

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39

Thapa, Mandira. "Optimal Feature Selection for Spatial Histogram Classifiers." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1513710294627304.

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40

Teixeira, Filipe. "Boosting compression-based classifiers for authorship attribution." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18375.

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Mestrado em Engenharia de Computadores e Telemática<br>Atribuição de autoria é o ato de atribuir um autor a documento anónimo. Apesar de esta tarefa ser tradicionalmente feita por especialistas, muitos novos métodos foram apresentados desde o aparecimento de computadores, em meados do século XX, alguns deles recorrendo a compressores para encontrar padrões recorrentes nos dados. Neste trabalho vamos apresentar os resultados que podem ser alcançados ao utilizar mais do que um compressor, utilizando um meta-algoritmo conhecido como Boosting.<br>Authorship attribution is the task of assign

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41

Rigaki, Maria. "Adversarial Deep Learning Against Intrusion Detection Classifiers." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64577.

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Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluring than ever. The intention of this thesis is to show that using machine learning in the intrusion detection domain should be accompanied with an evaluation of its robustness against adve

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42

Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096/document.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures

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Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures

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44

Lay, Nathan. "Supervised aggregation of classifiers using artificial prediction markets." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11082009-145713/.

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Thesis (M.S.)--Florida State University, 2009.<br>Advisor: Adrian Barbu, Florida State University, College of Arts and Sciences, Dept. of Scientific Computing. Title and description from dissertation home page (viewed on May 12, 2010). Document formatted into pages; contains vii, 46 pages. Includes bibliographical references.

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Ng, Siu-cheung. "Linguistic features of Cantonese classifiers in medical description." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31573290.

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Joe-Yen, Stefan. "Performance Envelopes of Adaptive Ensemble Data Stream Classifiers." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1014.

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This dissertation documents a study of the performance characteristics of algorithms designed to mitigate the effects of concept drift on online machine learning. Several supervised binary classifiers were evaluated on their performance when applied to an input data stream with a non-stationary class distribution. The selected classifiers included ensembles that combine the contributions of their member algorithms to improve overall performance. These ensembles adapt to changing class definitions, known as “concept drift,” often present in real-world situations, by adjusting the relative contr

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Song, Qing. "Features and statistical classifiers for face image analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0035/NQ62459.pdf.

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48

Tahseen, Tarkan. "A new approach to learning Bayesian network classifiers." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299378.

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Duangsoithong, Rakkrit. "Feature selection and casual discovery for ensemble classifiers." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.580345.

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With rapid development of computer and information technology that can improve a large number of applications such as web text mining, intrusion detection, biomedical informatics, gene selection in micro array data, medical data mining, and clinical de- cision support systems, many information databases have been created. However, in some applications especially in the medical area, clinical data may contain hundreds to thousands of features with relatively few samples. A consequence of this problem is increased complexity that leads to degradation in efficiency and accuracy. Moreover, in this

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Ng, Siu-cheung, and 吳劭璋. "Linguistic features of Cantonese classifiers in medical description." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31573290.

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