Instruction to Statistical Pattern Recognition
Free Global Shipping
No minimum orderDescription
Introduction to Statistical Pattern Recognition introduces the reader to statistical pattern recognition, with emphasis on statistical decision and estimation. Pattern recognition problems are discussed in terms of the eigenvalues and eigenvectors. Comprised of 11 chapters, this book opens with an overview of the formulation of pattern recognition problems. The next chapter is devoted to linear algebra, with particular reference to the properties of random variables and vectors. Hypothesis testing and parameter estimation are then discussed, along with error probability estimation and linear classifiers. The following chapters focus on successive approaches where the classifier is adaptively adjusted each time one sample is observed; feature selection and linear mapping for one distribution and multidistributions; and problems of nonlinear mapping. The final chapter describes a clustering algorithm and considers criteria for both parametric and nonparametric clustering. This monograph will serve as a text for the introductory courses of pattern recognition as well as a reference book for practitioners in the fields of mathematics and statistics.
Table of Contents
Preface
Acknowledgments
Chapter 1 Introduction
1.1 Formulation of Pattern Recognition Problems
1.2 Chapter Outlines
Chapter 2 Random Vectors and Their Properties
2.1 Random Vectors and Their Distributions
2.2 Properties of Distributions
2.3 Transformation of Random Vectors
2.4 Various Properties of Eigenvalues and Eigenvectors
Standard Data
Computer Projects
Problems
Chapter 3 Hypothesis Testing
3.1 Simple Hypothesis Tests
3.2 Error Probability in Hypothesis Testing
3.3 Upper Bounds on Error Probability
3.4 Other Hypothesis Tests
3.5 Sequential Hypothesis Testing
Computer Projects
Problems
Chapter 4 Linear Classifiers
4.1 The Bayes Linear Classifier
4.2 Linear Discriminant Function for Minimum Error
4.3 Linear Discriminant Function for Minimum Mean-Square Error
4.4 Desired Output and Mean-Square Error
4.5 Other Discriminant Functions
Computer Projects
Problems
Chapter 5 Parameter Estimation
5.1 Estimation of Nonrandom Parameters
5.2 Estimation of Random Parameters
5.3 Interval Estimation
5.4 Estimation of the Probability of Error
Appendix 5-1 Calculation of the Bias between the C Method and the Leaving-One-Out Method
Computer Projects
Problems
Chapter 6 Estimation of Density Functions
6.1 Parzen Estimate
6.2 k-Nearest Neighbor Approach
6.3 Histogram Approach
6.4 Expansion by Basis Functions
Computer Projects
Problems
Chapter 7 Successive Parameter Estimation
7.1 Successive Adjustment of a Linear Classifier
7.2 Stochastic Approximation
7.3 Successive Bayes Estimation
Computer Projects
Problems
Chapter 8 Feature Selection and Linear Mapping for One Distribution
8.1 The Discrete Karhunen-Loève Expansion
8.2 Other Criteria for One Distribution
8.3 The Karhunen-Loève Expansion for Random Processes
8.4 Estimation of Eigenvalues and Eigenvectors
APPENDIX 8-1 Calculation of E{(ΦiTŜΦj)2}
APPENDIX 8-2 Rapid Eigenvalue-Eigenvector Calculation
Computer Projects
Problems
Chapter 9 Feature Selection and Linear Mapping for Multidistributions
9.1 General Properties of Class Separability
9.2 Discriminant Analysis
9.3 The Chernoff Bound and the Bhattacharyya Distance
9.4 Divergence
Computer Projects
Problems
Chapter 10 Nonlinear Mapping
10.1 Intrinsic Dimensionality of Data
10.2 Separability Enhancement by Nonlinear Mapping
10.3 Two-Dimensional Displays
Computer Projects
Chapter 11 Clustering
11.1 An Algorithm for Clustering
11.2 Parametric Clustering Criteria
11.3 Nonparametric Clustering Criteria
11.4 Additional Clustering Procedures
Computer Projects
References
Index
Product details
- No. of pages: 386
- Language: English
- Copyright: © Academic Press 1972
- Published: January 1, 1972
- Imprint: Academic Press
- eBook ISBN: 9780323162784
About the Editor
Keinosuke Fukunaga
Ratings and Reviews
There are currently no reviews for "Instruction to Statistical Pattern Recognition"