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Introduction to Statistical Pattern Recognition : Computer Science and Scientific Computing Series - Keinosuke Fukunaga

Introduction to Statistical Pattern Recognition

Computer Science and Scientific Computing Series

Hardcover Published: 25th October 1990
ISBN: 9780122698514
Number Of Pages: 592

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This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Preface Acknowledgments Chapter 1 Introduction 1.1 Formulation of Pattern Recognition Problems 1.2 Process of Classifier Design Notation References Chapter 2 Random Vectors and Their Properties 2.1 Random Vectors and Their Distributions 2.2 Estimation of Parameters 2.3 Linear Transformation 2.4 Various Properties of Eigenvalues and Eigenvectors Computer Projects Problems References Chapter 3 Hypothesis Testing 3.1 Hypothesis Tests for Two Classes 3.2 Other Hypothesis Tests 3.3 Error Probability in Hypothesis Testing 3.4 Upper Bounds on the Bayes Error 3.5 Sequential Hypothesis Testing Computer Projects Problems References Chapter 4 Parametric Classifiers 4.1 The Bayes Linear Classifier 4.2 Linear Classifier Design 4.3 Quadratic Classifier Design 4.4 Other Classifiers Computer Projects Problems References Chapter5 Parameter Estimation 5.1 Effect of Sample Size in Estimation 5.2 Estimation of Classification Errors 5.3 Holdout, Leave-One-Out, and Resubstitution Methods 5.4 Bootstrap Methods Computer Projects Problems References Chapter 6 Nonparametric Density Estimation 6.1 Parzen Density Estimate 6.2 kNearest Neighbor Density Estimate 6.3 Expansion by Basis Functions Computer Projects Problems References Chapter 7 Nonparametric Classification and Error Estimation 7.1 General Discussion 7.2 Voting kNN Procedure - Asymptotic Analysis 7.3 Voting kNN Procedure - Finite Sample Analysis 7.4 Error Estimation 7.5 Miscellaneous Topics in the kNN Approach Computer Projects Problems References Chapter 8 Successive Parameter Estimation 8.1 Successive Adjustment of a Linear Classifier 8.2 Stochastic Approximation 8.3 Successive Bayes Estimation Computer Projects Problems References Chapter 9 Feature Extraction and Linear Mapping for Signal Representation 9.1 The Discrete Karhunen-Loeve Expansion 9.2 The Karhunen-Loeve Expansion for Random Processes 9.3 Estimation of Eigenvalues and Eigenvectors Computer Projects Problems References Chapter 10 Feature Extraction and Linear Mapping for Classification 10.1 General Problem Formulation 10.2 Discriminant Analysis 10.3 Generalized Criteria 10.4 Nonparametric Discriminant Analysis 10.5 Sequential Selection of Quadratic Features 10.6 Feature Subset Selection Computer Projects Problems References Chapter 11 Clustering 11.1 Parametric Clustering 11.2 Nonparametric Clustering 11.3 Selection of Representatives Computer Projects Problems References Appendix A Derivatives of Matrices Appendix B Mathematical Formulas Appendix C Normal Error Table Appendix D Gamma Function Table Index

ISBN: 9780122698514
ISBN-10: 0122698517
Series: Computer Science and Scientific Computing Series
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 592
Published: 25th October 1990
Publisher: Elsevier Science Publishing Co Inc
Country of Publication: US
Dimensions (cm): 22.9 x 15.2  x 3.18
Weight (kg): 0.94
Edition Number: 2
Edition Type: Revised