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.

part I|196 pages

Pattern Recognition

part II|72 pages

Neural Networks for Pattern Recognition

chapter 8|24 pages

Multilayer Perceptron

chapter |11 pages

Radial Basis Function Networks

chapter |12 pages

The Hopfield Model

part III|240 pages

Data Preprocessing for Pictorial Pattern Recognition

chapter 12|92 pages

Preprocessing in the Spatial Domain

chapter |28 pages

Wavelets and Wavelet Transform

part IV|52 pages


chapter 16|49 pages

Exemplary Applications

part V|12 pages

Practical Concerns of Image Processing and Pattern Recognition