Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this title discusses dimensionality reduction and feature selection; computer system architectures; algorithms for solutions to common roadblocks in data processing; and, computing models including the Hamming net and the Hopfield net.



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.