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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Chris Bishop is at Aston University.
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Descrizione libro Oxford University Press, 1996. Hardcover. Condizione libro: Good. Good condition, some are ex-library and can have markings. Codice libro della libreria GD-226-99-7605107
Descrizione libro Oxford University Press, 1996. Hardcover. Condizione libro: Good. Item may show signs of shelf wear. Pages may include limited notes and highlighting. Includes supplemental or companion materials if applicable. Access codes may or may not work. Connecting readers since 1972. Customer service is our top priority. Codice libro della libreria mon0000773079