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    Pattern Recognition and Neural Networks (Paperback) By (author) Brian D. Ripley

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    DescriptionThis 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.


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  • Full bibliographic data for Pattern Recognition and Neural Networks

    Title
    Pattern Recognition and Neural Networks
    Authors and contributors
    By (author) Brian D. Ripley
    Physical properties
    Format: Paperback
    Number of pages: 416
    Width: 189 mm
    Height: 246 mm
    Thickness: 22 mm
    Weight: 740 g
    Language
    English
    ISBN
    ISBN 13: 9780521717700
    ISBN 10: 0521717701
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 03
    BIC subject category V2: PBT
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    LC classification: QA
    B&T Modifier: Text Format: 06, 01
    BIC subject category V2: UYQP
    LC subject heading:
    Warengruppen-Systematik des deutschen Buchhandels: 26280
    BIC subject category V2: UYQN
    BISAC V2.8: MAT029000
    Ingram Subject Code: XG
    Libri: I-XG
    Abridged Dewey: 519
    B&T Merchandise Category: UP
    DC22: 006.4
    BISAC V2.8: COM047000, COM042000
    DC20: 006.4
    BISAC V2.8: COM044000
    LC classification: QA76.87 .R56 1996
    Thema V1.0: PBT, UYQN, UYQP
    Edition
    1
    Illustrations note
    41 b/w illus.
    Publisher
    CAMBRIDGE UNIVERSITY PRESS
    Imprint name
    CAMBRIDGE UNIVERSITY PRESS
    Publication date
    28 January 2008
    Publication City/Country
    Cambridge
    Author Information
    Brian Ripley is the Professor of Applied Statistics at the University of Oxford and a member of the Department of Statistics as well as a Professorial Fellow of St. Peter's College.
    Review quote
    'The combination of theory and examples makes this a unique and interesting book.' A. Gelman, Journal of the International Statistical Institute 'I can warmly recommend this book. Every researcher will benefit by the broadness of Ripley's view and the comprehensive bibliography.' Dee Denteneer, ITW Nieuws '... a grand overview of both the theory and the practice of the field ... of benefit to anyone who has an interest in a principled approach to statistical data analysis ... will indeed provide an excellent reference for many years to come.' Stephen Roberts, The Times Higher Education Supplement '... an excellent text on the statistics of pattern classifiers and the application of neural network techniques ... Ripley has managed ... to produce an altogether accessible text ...[it] will be rightly popular with newcomers to the area for its ability to present the mathematics of statistical pattern recognition and neural networks in an accessible format and engaging style.' Nature '... a valuable reference for engineers and science researchers.' Optics and Photonics News
    Back cover copy
    Known for his hype-free approach to neural networks, Brian Ripley here provides an excellent text on the statistics of pattern classifiers and the application of neural techniques...Ripley's text will be rightly popular with newcomers to the area for its ability to present the mathematics of statistical pattern recognition and neural networks in an accessible format and engaging style.
    Table of contents
    1. Introduction and examples; 2. Statistical decision theory; 3. Linear discriminant analysis; 4. Flexible discriminants; 5. Feed-forward neural networks; 6. Non-parametric methods; 7. Tree-structured classifiers; 8. Belief networks; 9. Unsupervised methods; 10. Finding good pattern features; Appendix: statistical sidelines; Glossary; References; Author index; Subject index.