Table of Contents

Preface
List of Tables
Part I. FUNDAMENTALS OF PATTERN RECOGNITION
0.: Basic Concepts of Pattern Recognition
1.: Decision-Theoretic Algorithms
2.: Structural Pattern Recognition
Part II. INTRODUCTORY NEURAL NETWORKS
3.: Artificial Neural Network Structures
4.: Supervised Training via Error Backpropagation: Derivations
PART III. ADVANCED FUNDAMENTALS OF NEURAL NETWORKS
5.: Acceleration and Stabilization of Supervised Gradient Training of MLPs
6.: Supervised Training via Strategic Search
7.: Advances in Network Algorithms for Classification and Recognition
8.: Recurrent Neural Networks
PART IV. NEURAL, FEATURE, AND DATA ENGINEERING
9.: Neural Engineering and Testing of FANNs
10.: Feature and Data Engineering
PART IV. TESTING AND APPLICATIONS
11.: Some Comparative Studies of Feedforward Artificial Neural Networks
12.: Pattern Recognition Applications

Reviews

"Pattern Recognition Using Neural Networks makes its subject easy to understand by offering intuitive explanations and examples. ...an excellent resource for those who want to implement neural networks, rather than just learn the theory."--Mark Kvale,
"Really good text for students and professionals."--Aiy Farag, University of Louisville