Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series) (Hardcover)


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Product Description
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.


About the Author
By Keinosuke Fukunaga


Product Details

• Hardcover: 592 pages
• Publisher: Academic Press; 2 edition (October 12, 1990)
• Language: English
• ISBN-10: 0122698517
• ISBN-13: 978-0122698514
• Product Dimensions: 9.2 x 6.1 x 1.3 inches

Introduction to Statistical Pattern Recognition, Second Edition | Publisher: Academic Press | ISBN: 0122698517 | edition 1990 | PDF | 616 pages | 12,5 mb



Contents

Preface ............................................. xi
Acknowledgments .................................. xm

Chapter 1 Introduction 1
1.1 Formulation of Pattern Recognition Problems ......... 1
1.2 Process of Classifier Design ........................ 7
Notation ........................................ 9
References ..................................... 10

Chapter2 Random Vectors and Their Properties 11
2.1 Random Vectors and Their Distributions ............. 11
2.2 Estimation of Parameters ......................... 17
2.3 Linear Transformation ........................... 24
2.4 Various Properties of Eigenvalues and Eigenvectors ................................... 35
Computer Projects ............................... 47
Problems ....................................... 48
References ..................................... 50

Chapter 3 Hypothesis Testing 51
3.1 Hypothesis Tests for Two Classes ................... 51
3.2 Other Hypothesis Tests ........................... 65
3.3 Error Probability in Hypothesis Testing ............. 85
3.4 Upper Bounds on the Bayes Error .................. 97
3.5 Sequential Hypothesis Testing .................... 110
Computer Projects .............................. 119
Problems ...................................... 120
References .................................... 122

Chapter 4 Parametric Classifiers 124
4.1 The Bayes Linear Classifier ....................... 125
4.2 Linear Classifier Design ......................... 131
4.3 Quadratic Classifier Design ...................... 153
4.4 Other Classifiers ................................ 169
Computer Projects .............................. 176
Problems ...................................... 177
References ..................................... 180

Chapter 5 Parameter Estimation 181
5.1 Effect of Sample Size in Estimation ................ 182
5.2 Estimation of Classification Errors ................ 196
5.3 Holdout. LeaveOneOut. and Resubstitution Methods ...................................... 219
5.4 Bootstrap Methods ............................. 238
Computer Projects .............................. 250
Problems ...................................... 250
References .................................... 252

Chapter 6 Nonparametric Density Estimation 254
6.1 Parzen Density Estimate ........................ 255
6.2 kNearest Neighbor Density Estimate .............. 268
6.3 Expansion by Basis Functions .................... 287
Computer Projects .............................. 295
Problems ..................................... 296
References .................................... 297

Chapter 7 Nonparametric Classification and Error Estimation 300
7.1 General Discussion .............................. 301
7.2 Voting kNN Procedure - Asymptotic Analysis ...... 305
7.3 Voting kNN Procedure - Finite Sample Analysis ..... 313
7.4 Error Estimation ............................... 322
7.5 Miscellaneous Topics in the kNN Approach .......... 351
Computer Projects .............................. 362
Problems ...................................... 363
References ..................................... 364

Chapter 8 Successive Parameter Estimation 367
8.1 Successive Adjustment of a Linear Classifier ........ 367
8.2 Stochastic Approximation ....................... 375
8.3 Successive Bayes Estimation ..................... 389
Computer Projects ............................ 395
Problems .................................... 396
References ................................... 397

Chapter 9 Feature Extraction and Linear Mapping for Signal Representation 399
9.1 The Discrete Karhunen-Lokve Expansion ........... 400
9.2 The Karhunen-LoBve Expansion for Random Processes ..................................... 417
9.3 Estimation of Eigenvalues and Eigenvectors . . . . . . . . 425
Computer Projects .............................. 435
Problems ..................................... 438
References .................................... 440

Chapter 10 Feature Extraction and Linear Mapping for Classification 441
10.1 General Problem Formulation .................... 442
10.2 Discriminant Analysis ......................... 445
10.3 Generalized Criteria ............................ 460
10.4 Nonparametric Discriminant Analysis . . . . . . . . . . . . 466
10.5 Sequential Selection of Quadratic Features . . . . . . . . . 480
10.6 Feature Subset Selection ........................ 489
Computer Projects ............................. 503
Problems ..................................... 504
References .................................... 506

Chapter 11 Clustering 508
11.1 Parametric Clustering .......................... 509
11.2 Nonparametric Clustering ....................... 533
11.3 Selection of Representatives ..................... 549
Computer Projects ............................. 559
Problems ..................................... 560
References .................................... 562

Appendix A DERIVATIVES OF MATRICES ............. 564
Appendix B MATHEMATICAL FORMULAS ............ 572
Appendix C NORMAL ERROR TABLE .................576
Appendix D GAMMA FUNCTION TABLE .............. 578
Index ................................................ 579