Pattern Recognition and Neural Networks (Hardcover)

by Brian D. Ripley (Author) "This book is primarily about pattern recognition, which covers a wide range of activities from many walks of life..." (more)
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Editorial Reviews

Amazon.com Review
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.

Review
"...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 & Photonics News

"The combination of theory and examples makes this a unique and interesting book." International Statistical Institute Journal

See all Editorial Reviews

Product Details

• Hardcover: 415 pages
• Publisher: Cambridge University Press (January 26, 1996)
• Language: English
• ISBN-10: 0521460867
• ISBN-13: 978-0521460866
• Product Dimensions: 9.7 x 7.5 x 1 inches
• Shipping Weight: 2.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars See all reviews (9 customer reviews)
• Amazon.com Sales Rank: #1,097,402 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

27 of 27 people found the following review helpful:

5.0 out of 5 stars advanced and important work, June 11, 2001

By	Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews (REAL NAME)

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.

22 of 24 people found the following review helpful:

5.0 out of 5 stars important and well developed approaches to pattern recognition and machine learning through neural nets., January 24, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

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 of 18 people found the following review helpful:

5.0 out of 5 stars A synthesis, not an introduction, September 29, 2000

By A Customer

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.