Neural Networks for Pattern Recognition by Christopher M. Bishop (English) Paper

Description: Neural Networks for Pattern Recognition by Christopher M. Bishop, Geoffrey Hinton This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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, andreviews 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 topicsof data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks. Author Biography Chris Bishop is the author of German Panzers of World War II, The Rise of Hitlers Third Reich, SS: Hitlers Foreign Divisions, The Essential Tank Identification Guide: Wehrmacht Panzer Divisions, 1939-45, The Essential Aircraft Identification Guide: Luftwaffe Squadrons, 1939-45 and The Essential Submarine Identification Guide: Kriegsmarine U-Boats, 1939-45. Chris Bishop lives in England. Table of Contents 1: Statistical pattern recognition2: Probability density estimation3: Single-layer networks4: The multi-layer perceptron5: Radial basis functions6: Error functions7: Parameter optimization algorithms8: Pre-processing and feature extraction9: Learning and generalization10: Bayesian techniques Review excellent... Bishop is able to achieve a level of depth on these topics which is unparalleled in other neural-net texts.... clear and concise mathematical analysis. Bishops text [] picks up where Duda and Hart left off, and, luckily does so with the same level of clarity and elegance. Neural Networks for Pattern Recognition is an excellent read, and represents a real contribution to the neural-net community. IEEE Transactions on Neural Networks,May 1997`this is an excellent book in the specialised area of statistical pattern recognition with statistical neural nets ... a good starting point for new students in those laboratories where research into statistico-neural pattern recognition is being done ... The examples for the reader at the end of this and every chapter are well chosen and will ensure sales as a course textbook ... this is a first-class book for the researcher in statistical patternrecognition.Times HigherBishop leads the way through a forest of mathematical minutiae. Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition. New Scientist[Bishop] has written a textbook, introducing techniques, relating them to the theory, and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book.... should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science. The Computer Journal, Volume 39, No. 6, 1996Its sequential organization and end-of chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour. Scientific Computing World`a neural network introduction placed in a pattern recognition context. ...He has written a textbook, introducing techniques, relating them to the theory and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book ... should be warmly welcomed by the neural network and pattern recognition communities.Robert P. W. Duin, IAPR Newsletter Vol. 19 No. 2 April 1997`This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition.Zbl.Math 868 Promotional The first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. Long Description 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, andreviews 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 topicsof data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks. Review Text excellent... Bishop is able to achieve a level of depth on these topics which is unparalleled in other neural-net texts.... clear and concise mathematical analysis. Bishops text [] picks up where Duda and Hart left off, and, luckily does so with the same level of clarity and elegance. Neural Networks for Pattern Recognition is an excellent read, and represents a real contribution to the neural-net community. IEEE Transactions on Neural Networks,May 1997`this is an excellent book in the specialised area of statistical pattern recognition with statistical neural nets ... a good starting point for new students in those laboratories where research into statistico-neural pattern recognition is being done ... The examples for the reader at the end of this and every chapter are well chosen and will ensure sales as a course textbook ... this is a first-class book for the researcher in statistical patternrecognition.Times HigherBishop leads the way through a forest of mathematical minutiae. Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition. New Scientist[Bishop] has written a textbook, introducing techniques, relating them to the theory, and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book.... should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science. The Computer Journal, Volume 39, No. 6, 1996Its sequential organization and end-of chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour. Scientific Computing World`a neural network introduction placed in a pattern recognition context. ...He has written a textbook, introducing techniques, relating them to the theory and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book ... should be warmly welcomed by the neural network and pattern recognition communities. Robert P. W. Duin, IAPR Newsletter Vol. 19 No. 2 April 1997`This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition.Zbl.Math 868 Review Quote This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition.Zbl.Math 868 Details ISBN0198538642 Short Title NEURAL NETWORKS FOR PATTERN RE Pages 504 Language English ISBN-10 0198538642 ISBN-13 9780198538646 Media Book Format Paperback Year 1995 Place of Publication Oxford Country of Publication United Kingdom Edition 1st Series Advanced Texts in Econometrics (Paperback) DOI 10.1604/9780198538646 UK Release Date 1995-11-23 NZ Release Date 1995-11-23 Author Geoffrey Hinton Publisher Oxford University Press Publication Date 1995-11-23 DEWEY 006.42 Illustrations line figures Audience Professional & Vocational AU Release Date 1995-11-22 Imprint Oxford University Press We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 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