Pattern Classification

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.

Table of Contents

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

About the Author

Peter E. Hart‘s technical contributions underlie some of the most widely-used procedures in modern computing, such as the procedure that computes your driving directions, the nearest neighbor rule for pattern classification, and a widely used procedure for detecting geometric patterns in digital images.

Beyond his research contributions, Dr. Hart has been a founder or head of four companies and three international research centers. He occasionally serves as an expert witness and consults on technology strategy. Hart holds over 125 US and foreign patents, and is a Fellow of the IEEE, the ACM and the AAAI.

Hart is also a furniture designer and maker, and has completed over 75 cycling centuries.