Pattern Recognition and Neural Networks

Author: Brian D. Ripley List Price: $75.00 Our Price: Click to see the latest and low price ISBN: 0521460867 Publisher: Cambridge University Press (18 January, 1996) Edition: Hardcover Sales Rank: 72,673 Average Customer Rating: 4.12 out of 5

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Customer Reviews

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 perfect second book on the subject.

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.

The neural network section explains the workings of NNs that are typically hidden to users of NNs in software packages. In some cases a click of a button is all that is needed to do what is explained in considerable depth in this tome. It can be very useful to fully understand what it is that has happened when a program switch is altered. This prevents using a NN and receiving a naive result that makes unfounded predictions.

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.

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