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Installieren Sie die genialokal App auf Ihrem Startbildschirm für einen schnellen Zugriff und eine komfortable Nutzung.
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.
Sing T. Bow
Pattern recognition: supervised and unsupervised learning in pattern recognition; nonparametric decision theoretic classification; nonparametric (distribution-free) training of discriminant functions; statistical discriminant functions; clusteringanalysis and unsupervised learning; dimensionality reduction and feature selection. Neural networks for pattern recognition: multilayer perception; radial basis function networks; hamming net and Kohonen self-organizing feature map; the Hopfield model.Data preprocessing for pictorial pattern recognition: preprocessing in the spatial domain; pictorial data preposessing and shape analysis; transforms and image processing in the transform doamin; wavelets and wavelet transforms. Applications: exemplaryapplications. Practical concerns of image processing and pattern recognition: computer system architectures for image processing and pattern recognition. Appendices: digital images; image model and discrete mathematics; digital image fundamentals; matrixmanipulation; Eigenvectors and Eigenvalves of an operator; notation.
