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    Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback)) (Paperback) By (author) C.M. Bishop, Volume editor Geoffrey E. Hinton, By (author) Geoffrey Hinton

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    DescriptionThis 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.


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    Title
    Neural Networks for Pattern Recognition
    Authors and contributors
    By (author) C.M. Bishop, Volume editor Geoffrey E. Hinton, By (author) Geoffrey Hinton
    Physical properties
    Format: Paperback
    Number of pages: 504
    Width: 155 mm
    Height: 229 mm
    Thickness: 28 mm
    Weight: 748 g
    Language
    English
    ISBN
    ISBN 13: 9780198538646
    ISBN 10: 0198538642
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 03
    BISAC V2.8: LAN009000
    B&T General Subject: 431
    Warengruppen-Systematik des deutschen Buchhandels: 16900
    BISAC V2.8: HIS014000
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06, 01
    BIC subject category V2: UYQP
    LC subject heading:
    BIC subject category V2: UYQN
    Ingram Subject Code: XG
    B&T Merchandise Category: UP
    BISAC V2.8: COM079010
    LC classification: QA76.87.B5
    LC subject heading:
    DC22: 006.4
    BISAC V2.8: COM047000
    LC subject heading:
    DC21: 006.4
    B&T Approval Code: A93490000, A93203605
    BISAC V2.8: COM044000
    LC classification: QA76.87 .B574 1995
    Thema V1.0: CF, UYZ, UYQN, UYQP
    Illustrations note
    line figures
    Publisher
    Oxford University Press
    Imprint name
    Clarendon Press
    Publication date
    18 January 1996
    Publication City/Country
    Oxford
    Review quote
    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. Bishop's 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 pattern recognition. Times Higher Bishop 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, 1996 Its 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
    Table of contents
    1. Statistical pattern recognition ; 2. Probability density estimation ; 3. Single-layer networks ; 4. The multi-layer perceptron ; 5. Radial basis functions ; 6. Error functions ; 7. Parameter optimization algorithms ; 8. Pre-processing and feature extraction ; 9. Learning and generalization ; 10. Bayesian techniques