Articles liés à Pattern Recognition and Neural Networks
Synopsis
Book by Ripley Brian D
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Revue de presse
'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
Présentation de l'éditeur
This 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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurCambridge University Press
- Date d'édition2008
- ISBN 10 0521717701
- ISBN 13 9780521717700
- ReliureBroché
- Langueanglais
- Numéro d'édition1
- Nombre de pages416
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Pattern Recognition and Neural Networks
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Pattern Recognition and Neural Networks
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ISBN 13 : 9780521717700
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Paperback. Etat : new. Paperback. This 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: /~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. This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from /~ripley/PRbook/ Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780521717700
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