Sinossi
This is a comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the topics of data processing, feature extraction and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
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L'autore
Chris Bishop is at Aston University.
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Neural Networks for Pattern Recognition
Editore:
Oxford University Press, 1996
ISBN 10: 0198538499
ISBN 13: 9780198538493
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Da: -OnTimeBooks-, Phoenix, AZ, U.S.A.
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Condizione: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item for full refund. Ships USPS Media Mail. Codice articolo OTV.0198538499.VG
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