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008 140422s2014 nju ob 001 0 eng
010 _a 2014016123
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035 _a(OCoLC)878051089
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_z(OCoLC)961585666
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049 _aMAIN
100 1 _aKuncheva, Ludmila I.
_q(Ludmila Ilieva),
_d1959-
245 1 0 _aCombining pattern classifiers :
_bmethods and algorithms /
_cLudmila I. Kuncheva.
250 _aSecond edition.
264 1 _aHoboken, NJ :
_bWiley,
_c2014.
300 _a1 online resource (xxi, 357 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
504 _aIncludes bibliographical references and index.
520 _a"Combined classifiers, which are central to the ubiquitous performance of pattern recognition and machine learning, are generally considered more accurate than single classifiers. In a didactic, detailed assessment, Combining Pattern Classifiers examines the basic theories and tactics of classifier combination while presenting the most recent research in the field. Among the pattern recognition tasks that this book explores are mail sorting, face recognition, signature verification, decoding brain fMRI images, identifying emotions, analyzing gene microarray data, and spotting patterns in consumer preference. This updated second edition is equipped with the latest knowledge for academics, students, and practitioners involved in pattern recognition fields"--
_cProvided by publisher.
520 _a"Classifier Combination is a field of growing interest within the very large area of Pattern Classification"--
_cProvided by publisher.
588 0 _aPrint version record and CIP data provided by publisher.
505 0 _a""Titlepage""; ""Copyright""; ""Dedication""; ""Preface""; ""The Playing Field""; ""Software""; ""Structure and What is New in the Second Edition""; ""Who is This Book For?""; ""Notes""; ""Acknowledgements""; ""1 Fundamentals of Pattern Recognition""; ""1.1 Basic Concepts: Class, Feature, Data Set""; ""1.2 Classifier, Discriminant Functions, Classification Regions""; ""1.3 Classification Error and Classification Accuracy""; ""1.4 Experimental Comparison of Classifiers""; ""1.5 Bayes Decision Theory""; ""1.6 Clustering and Feature Selection""; ""1.7 Challenges of Real-Life Data""; ""Appendix""
505 8 _a""1.A.1 Data Generation""""1.A.2 Comparison of Classifiers""; ""1.A.3 Feature Selection""; ""Notes""; ""2 Base Classifiers""; ""2.1 Linear and Quadratic Classifiers""; ""2.2 Decision Tree Classifiers""; ""2.3 The Na�A�ve Bayes Classifier""; ""2.4 Neural Networks""; ""2.5 Support Vector Machines""; ""2.6 The k-Nearest Neighbor Classifier (k-nn)""; ""2.7 Final Remarks""; ""Appendix""; ""2.A.1 Matlab Code for the Fish Data""; ""2.A.2 Matlab Code for Individual Classifiers""; ""Notes""; ""3 An Overview of the Field""; ""3.1 Philosophy""; ""3.2 Two Examples""; ""3.3 Structure of the Area""
505 8 _6880-01
_a""5.3 Nontrainable (Fixed) Combination Rules""""5.4 The Weighted Average (Linear Combiner)""; ""5.5 A Classifier as a Combiner""; ""5.6 An Example of Nine Combiners for Continuous-Valued Outputs""; ""5.7 To Train or Not to Train?""; ""Appendix""; ""5.A.1 Theoretical Classification Error for the Simple Combiners""; ""5.A.2 Selected Matlab Code""; ""Notes""; ""6 Ensemble Methods""; ""6.1 Bagging""; ""6.2 Random Forests""; ""6.3 Adaboost""; ""6.4 Random Subspace Ensembles""; ""6.5 Rotation Forest""; ""6.6 Random Linear Oracle""; ""6.7 Error Correcting Output Codes (ECOC)""; ""Appendix""
505 8 _a""6.A.1 Bagging""""6.A.2 AdaBoost""; ""6.A.3 Random Subspace""; ""6.A.4 Rotation Forest""; ""6.A.5 Random Linear Oracle""; ""6.A.6 Ecoc""; ""Notes""; ""7 Classifier Selection""; ""7.1 Preliminaries""; ""7.2 Why Classifier Selection Works""; ""7.3 Estimating Local Competence Dynamically""; ""7.4 Pre-Estimation of the Competence Regions""; ""7.5 Simultaneous Training of Regions and Classifiers""; ""7.6 Cascade Classifiers""; ""Appendix: Selected Matlab Code""; ""7.A.1 Banana Data""; ""7.A.2 Evolutionary Algorithm for a Selection Ensemble for the Banana Data""
650 0 _aPattern recognition systems.
650 0 _aImage processing
_xDigital techniques.
650 7 _aTECHNOLOGY & ENGINEERING
_xImaging Systems.
_2bisacsh
650 7 _aCOMPUTERS
_xComputer Vision & Pattern Recognition.
_2bisacsh
650 7 _aCOMPUTERS
_xDatabase Management
_xData Mining.
_2bisacsh
650 7 _aImage processing
_xDigital techniques.
_2fast
_0(OCoLC)fst00967508
650 7 _aPattern recognition systems.
_2fast
_0(OCoLC)fst01055266
650 4 _aCOMPUTERS / Computer Vision & Pattern Recognition.
655 4 _aElectronic books.
655 0 _aElectronic books.
776 0 8 _iPrint version:
_aKuncheva, Ludmila I. (Ludmila Ilieva), 1959-
_tCombining pattern classifiers.
_bSecond edition.
_dHoboken, New Jersey : Wiley, [2014]
_z9781118315231
_w(DLC) 2014014214
_w(OCoLC)878050954
856 4 0 _uhttp://dx.doi.org/10.1002/9781118914564
880 8 _6505-01/Zsym
_a""3.4 Quo Vadis?""""Notes""; ""4 Combining Label Outputs""; ""4.1 Types of Classifier Outputs""; ""4.2 A Probabilistic Framework for Combining Label Outputs""; ""4.3 Majority Vote""; ""4.4 Weighted Majority Vote""; ""4.5 Na�A�ve-Bayes Combiner""; ""4.6 Multinomial Methods""; ""4.7 Comparison of Combination Methods for Label�A Outputs""; ""Appendix""; ""4.A.1 Matan�a�"s Proof for the Limits on the Majority Vote�A Accuracy""; ""4.A.2 Selected Matlab Code""; ""Notes""; ""5 Combining Continuous-Valued Outputs""; ""5.1 Decision Profile""; ""5.2 How Do We Get Probability Outputs?""
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