Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections

by Sethi, Ishwar K.; Jain, Anil K.

• ISBN13: 9780444887405
• ISBN10: 0444887407
• Format: Hardcover
• Copyright: 1991-08-01
• Publisher: Elsevier Science Ltd

Summary

With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Table of Contents

Foreword
Preface
ANN and SPR Relationship
Evaluation of a Class of Pattern-Recognition Networks	p. 1
Links between Artificial Neural Networks (ANN) and Statistical Pattern Recognition	p. 11
Small Sample Size Problem in Designing Artificial Neural Network	p. 33
On Tree Structured Classifier	p. 51
Decision Tree Performance Enhancement Using an Artificial Neural Network Implementation	p. 71
Applications
Bayesian and Neural Network Pattern Recognition: A Theoretical Connection and Empirical Results with Handwritten Characters	p. 89
Shape and Texture Recognition by a Neural Network	p. 109
Neural Networks for Textured Image Processing	p. 133
Markov Random Fields and Neural Networks with Applications to Early Vision Problems	p. 155
Connectionist Models and their Application to Automatic Speech Recognition	p. 175
Implementation Aspects
Dynamic Associative Memories	p. 195
Optical Associative Memories	p. 219
Artificial Neural Nets in MOS Silicon	p. 243
Author Index	p. 271
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