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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists
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Pattern Recognition Using Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer, which makes this a refreshing whatwhyandhow text that contrasts with the theoretical approach and pieinthesky hyperbole of many books on neural networks. It covers the standard decisiontheoretic pattern recognition of clustering via minimum distance, graphical and structural methods, and Bayesian discrimination.Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiplelayered perceptrons, functional link nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, selforganizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions. The more efficient fullpropagation, quickpropagation, cascade correlation, and various methods such as strategic search, conjugate gradients, and genetic algorithms are described. Advanced methods are also described, including the full training algorithms for radial basis function networks and random vector functional link nets, as well as competitive learning networks and fuzzy clustering algorithms.Special topics covered include:feature engineeringdata engineeringneural engineering of network architecturesvalidation and verification of the trained networksThis textbook is ideally suited for a senior undergraduate or graduate course in pattern recognition or neural networks for students in computer science, electrical engineering, and computer engineering. It is also a useful reference and resource for researchers and professionals.
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