Pattern Recognition and Image Preprocessing (Signal Processing and Communications)

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. Includes new and expanded sections on neural networks, Fisher’s discriminant, wavelet transform, and the method of principal components!Thoroughly revised and updated, the Second Edition of Pattern Recognition and Image Preprocessing containscurrent discussions on dimensionality reduction and feature selectionnovel 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 netdetailed appendices with data sets illustrating key concepts in the textthe methodology employed in preprocessing a large data-set problem, using illustrations such as sceneric imagesDescribing non-parametric and parametric theoretic classification and the training of discriminant functions, the Second Edition of Pattern Recognition and Image Preprocessing is an in-depth reference for electrical, electronics, optical, and industrial design engineers; applied mathematicians; computer scientists; and research and development personnel; and an informative text for upper-level undergraduate and graduate students in these disciplines. 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.