ABSTRACT
Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection; novel computer system architectures; proven algorithms for solutions to common roadblocks in data processing; computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net; detailed appendices with data sets illustrating key concepts in the text; and more.
TABLE OF CONTENTS
part I|196 pages
Pattern Recognition
part II|72 pages
Neural Networks for Pattern Recognition
part III|240 pages
Data Preprocessing for Pictorial Pattern Recognition
part IV|52 pages
Applications
part V|12 pages
Practical Concerns of Image Processing and Pattern Recognition