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Pattern Recognition and Neural Networks (Englisch) Taschenbuch – 10. Januar 2008

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Produktbeschreibungen

Amazon.de

This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. Almost all the results have proofs that are often original. On the application side, the emphasis is on pattern recognition. Most of the examples are from real world problems. In addition to the more common types of networks, the book has chapters on decision trees and belief networks from the machine-learning field. This book is intended for use in graduate courses that teach statistics and engineering. A strong background in statistics is needed to fully appreciate the theoretical developments and proofs. However, undergraduate-level linear algebra, calculus, and probability knowledge is sufficient to follow the book. -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.

Pressestimmen

'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

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Format: Gebundene Ausgabe
After reading the reviews I was really looking forward to reading this book, but ended up a bit disappointed. The editorial review and introduction lead me to believe that there was difficult material in the book, but I would be able to make my way through most of it.
I came at the book with a computer science background (and no prior neural network experience) and found the material rather difficult to follow. The statistics and math needed to really follow the book was more than I expected.
This doesn't mean the book is bad. After skimming through it a couple of times I really believe that the other reviewers are right -- this is a great resource on neural networks. However, just be sure you have the appropriate background to really get the most out of it.
If you are looking for an introductory book on neural nets or are a little rusty on your statistics and math I would recommend looking elsewhere.
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Format: Gebundene Ausgabe
This text has an extensive development of Neural networks
from a strong statistical basis. For anyone wanting a
quick way to access the broad spectrum of literature covering
neural networks and find the seminal papers, thoughts, developments
of the field, the literature references are worth the price.
This is essentially a literature survey, and not a "how-to"
book. It is not excessively heavy on the mathematics but
the uses verbiage to enhance the math that is necessary for
such a topic. It handles a number of significant but often
overlooked issues, such as the need for an ordering scheme of the partial
derivatives in backpropagation. Most authors don't address the obscure
but important points that will make or break your work if you
aren't aware of them. Ripley makes the reader cognizant of
where the minefields lie. This book is a Rosetta stone into
neural networks.
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Von Ein Kunde am 13. Februar 2000
Format: Gebundene Ausgabe
Neural Networks and the range of other techniques to come out of AI have never been given the statistical treatment that they need. Brian Ripley has created a book for a statistician such as myself that is informative and complete. It is interesting to see that many of the more traditional multivariate analysis techniques can be at least as good and better then some of the new AI versions. This book is a must have if NN and aligned technologies are to advance having some statistical measure of efficiency is essential
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