Introduction to Statistical Pattern Recognition (Computer Science & Scientific Computing) 2nd Edition

by Keinosuke Fukunaga  (Author)



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.

Table of Contents

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