BISHOP:NEURAL NETWORKS FOR PATTERN RECOGNITION PAPER (Advanced Texts in Econometrics (Paperback)) - Softcover
Synopsis
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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About the Author
Chris Bishop is at Aston University.
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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
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ISBN 13: 9780198538646
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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
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BISHOP:NEURAL NETWORKS FOR PATTERN RECOGNITION PAPER (Advanced Texts in Econometrics (Paperback))
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Oxford University Press, 1995
ISBN 10: 0198538642
ISBN 13: 9780198538646
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Neural Networks for Pattern Recognition
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BISHOP:NEURAL NETWORKS FOR PATTERN RECOGNITION PAPER (Advanced Texts in Econometrics (Paperback))
Published by
Oxford University Press, 1995
ISBN 10: 0198538642
ISBN 13: 9780198538646
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BISHOP:NEURAL NETWORKS FOR PATTERN RECOGNITION PAPER (Advanced Texts in Econometrics (Paperback))
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Oxford University Press, United Kingdom, Oxford, 1995
ISBN 10: 0198538642
ISBN 13: 9780198538646
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Paperback. Condition: Very Good. This book provides the first 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 important 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR003265806
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