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Pattern Recognition and Neural Networks [Englisch] [Taschenbuch]

Brian D. Ripley
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Kurzbeschreibung

10. Januar 2008
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.

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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 Educational 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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Kundenrezensionen

4.0 von 5 Sternen
4.0 von 5 Sternen
Die hilfreichsten Kundenrezensionen
4 von 4 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen I ended up returning it... 16. Mai 2000
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.
War diese Rezension für Sie hilfreich?
1 von 1 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen This book is a Rosetta stone into neural networks 14. März 1997
Von Ein Kunde
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.
War diese Rezension für Sie hilfreich?
1 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A definite must have 13. Februar 2000
Von Ein Kunde
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
War diese Rezension für Sie hilfreich?
Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 4.2 von 5 Sternen  9 Rezensionen
14 von 14 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen not for the faint at heart, but such a pleasure to read 2. März 2004
Von Boris Aleksandrovsky - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Let me start by saying that this book assumes a lot of background, especially in statistics. It dives into the math right away without even a hint or a gentle slope. But what I appreciate is that math is never used for its own sake, it is always justified. The book starts with the introduction to the problems neural nets are to be applied to - pattern recognition task. It proceeds to the elements of statistical decision theory, then goes up to linear discriminant analysis and perceptrons, then up you go to feed-forward neural nets. Non-parametric models and tree-based classifiers are covered next. Belief networks and unsupervised methods (MDS, clustering, etc..) follow. Coverage is extensive, although I would like to see more in the areas of unsupervised learning, which is quite foundational to the whole business.
What sells me on this book quite frankly is that is always keeps an eye on a real-world example. No model or algorithm is introduced without a real-world problem it was intended to solve. You would be better served by the Bishop book (Neural Networks for Pattern Recognition, by C.Bishop ISBN:0198538642) if you are looking for a quick introduction. I would say Ripley's book is the <it>perfect second book on the subject</it>.
I must aplaud the editors and designers of the book. A book itself, apart from the material it covers, is an aestetically most pleasent creation for the somewhat dry subject. Its use of margins is a piece of art - margins are wide, accessible, important points are highlighted there, and you can get to the needed point by flipping the pages quickly. The quality of paper is very good, the book opens wells, and holds its form very well. If you take it seriously and use it often, these qualities will gain in importance.
23 von 25 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen important and well developed approaches to pattern recognition and machine learning through neural nets. 24. Januar 2008
Von Michael R. Chernick - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
If you want a nice up-to-date treatment on neural networks and statistical pattern recognition with lots of nice pictures and an elementary treatment, I recommend the new edition of Duda and Hart. However, neural networks were basically started by the computer-science / artificial intelligence community using analogies to the human nervous system and the perceived connections to the human thought processes. These connections and arguments are weak.
However, a statistical theory of nonlinear classification algorithms shows that these methods have nice properties and have mathematical justification. The statistical pattern recognition research is well over 30 years old and is very well established. So these connections are important for putting neural networks on firm ground and providing greater acceptability from the statistical as well as the engineering community.

Ripley provides a theoretical threatment of the state-of-the-art in statistical pattern recognition. His treatment is thorough, covering all the important developments. He provides a large bibliography and a nice glossary of terms in the back of the book.

Recent papers on neural networks and data mining are often quick to generate results but not very good at providing useful validation techniques that show that perceived performance is not just an artifact of overfitting a model. This is an area where statisticians play a very important role, as they are keenly aware through their experience with regression modeling and prediction, of the crucial need for cross-validation. Ripley covers this quite clearly in Section 2.6 titled "How complex a model do we need?"

It is nice to see the thoroughness of this work. For example, in error rate estimation, many know of the advances of Lachenbruch and Mickey on error rate estimation in discriminant analysis and the further advances of Efron and others with the bootstrap. But in between there was also significant progress by Glick on smooth estimators. This work has been overlooked by many statisticians probably because some of it appears in the engineering literature (but one important paper was in the Journal of the American Statistical Association [JASA] in 1972). To some extent this oversight may be due to the fact that it was not mentioned in Efron's famous 1983 JASA paper and hence is usually missed in the bootstrap literature. Bootstrap methods and cross-validation play a prominent role in this text.

This is an excellent reference book for anyone seriously interested in pattern recognition research. For applied and theoretical statisticians who want a good account of the theory behind neural networks it is a must.
17 von 18 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A synthesis, not an introduction 29. September 2000
Von Ein Kunde - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
This text is wonderful. As some have pointed out, it might not be the best introduction to statistical pattern recognition and classification. Not every text should strive to be introductory, however, and this work shines for other reasons. The true strength of the book is its synthesis of material from diverse domains in a single text. My experience has been in the realm of statistics, and it was insightful to find that neural network approaches share much with traditional classification and discrimination techniques. I find the book enlighting not so much because it explains a given technique in great detail, but because it explains how a number of techniques are related and differ from one another. In this sense, it has opened up a whole new world of approaches to problems I encounter, that I had previously deemed inapplicable because they were "AI engineering techniques" or some such thing. If you want to learn about the details of a particular approach to pattern recognition--e.g., ICA, kohonen maps, SVM, etc.--find a different text. If you want an overview of the field of pattern recognition, however, buy this text. It provides a comprehensive, integrative perspective on classical and modern techniques from a number of disciplines. In fact, I would recommend this text as a complement to a more detailed text on a given pattern recognition technique--the one will fill in the details Ripley necessarily skims, and Ripley will explain how the technique is related to everything else.
25 von 29 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen I ended up returning it... 16. Mai 2000
Von Douglas Welzel - Veröffentlicht auf Amazon.com
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.
10 von 10 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen This book is a Rosetta stone into neural networks 14. März 1997
Von Ein Kunde - Veröffentlicht auf Amazon.com
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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