Introduction to Statistical Pattern Recognition. Edition No. 2

  • ID: 1767054
  • Book
  • 592 Pages
  • Elsevier Science and Technology
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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.
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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-Loéve Expansion


9.2 The Karhunen-Loéve 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
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Fukunaga, Keinosuke
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