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Pattern Classification
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About This Book
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
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Yes, you can access Pattern Classification by Richard O. Duda, Peter E. Hart, David G. Stork in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Electrical Engineering & Telecommunications. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Pattern Classification
- CONTENTS
- PREFACE
- 1 INTRODUCTION
- 2 BAYESIAN DECISION THEORY
- 3 MAXIMUM-LIKELIHOOD AND BAYESIAN PARAMETER ESTIMATION
- 4 NONPARAMETRIC TECHNIQUES
- 5 LINEAR DISCRIMINANT FUNCTIONS
- 6 MULTILAYER NEURAL NETWORKS
- 7 STOCHASTIC METHODS
- 8 NONMETRIC METHODS
- 9 ALGORITHM-INDEPENDENT MACHINE LEARNING
- 10 UNSUPERVISED LEARNING AND CLUSTERING
- A MATHEMATICAL FOUNDATIONS
- INDEX