Combining pattern classifiers : methods and algorithms / Ludmila I. Kuncheva.

Includes bibliographical references and index.

"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"-- Provided by publisher.

"Classifier Combination is a field of growing interest within the very large area of Pattern Classification"-- Provided by publisher.

Print version record and CIP data provided by publisher.

""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""

""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 NaÃv̄e 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""

880-01 ""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""

""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""