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Title: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman ISBN: 0-387-95284-5 Publisher: Springer Verlag Pub. Date: 09 August, 2001 Format: Hardcover Volumes: 1 List Price(USD): $79.95 |
Average Customer Rating: 4 (7 reviews)
Rating: 5
Summary: Counter to review from Sep 8
Comment: The review from September 8 expresses an opinion which is the exact opposite of mine, and is worded so strongly that I have to object. I gave a course using the book to bioinformaticians, most of them with a computer science background, and found the book exceptionally well prepared and suitable for a graduate course. The book serves the dual purpose of an introduction and a reference. An especially nice feature is how the authors explain the relationships and differences between different methods. By doing so, they provide context which I have not seen in any other book on this subject. The book is a very nice combination of basic theory and performance evaluation on data from a wide variety of domains and it is quite up-to-date. It has a well developed website going with it and the graphical material can be obtained electronically from the publisher. The book is an outstanding contribution to the field.
Rating: 1
Summary: Pedagogical Disaster
Comment: The Hastie book was used at our major university to teach data mining and statistical learning. The students in this graduate-level course included people with Masters and PhD degrees, as well as post-docs. Most people work in the field of bioinformatics, so have a pretty good grasp of complex topics and computer science, as well as mathematical algorithms. The overall rating from the course was a D-, which is one of the worst ratings for a book that was used on campus (out of hundreds). The text was hard to follow, confusing in many sections, and tough to teach from. It does cover a lot of ground, which is a benefit. But apparently the ability to do justice to clearly cover such breadth is a challenge that 20 really smart people couldn't figure out. Maybe individuals with a strong background and understanding in one or more of the areas covered by the book can do well by this item, but from a teaching/learning perspective there is at least one group of folks out here who would have done better with some other alternative.
Rating: 5
Summary: Excellent introduction to statistical learning
Comment: This book is an excellent survey of the huge area of statistics / computer science called statistical learning. The discussion is interesting and accurate, but not too theoretical. It is the best book to date for a general audience with a reasonable math/stat background. One of the strengths is the wide variety of topics covered; it is very comprehensive. If there is a weakness, it is that depth is limited. Plenty of references are provided for further study, and the authors maintain a website. Recommended as a reference or a starting point for an applied statistician or mathematician, or as a text for a first course in the subject.
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Title: Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth ISBN: 026208290X Publisher: MIT Press Pub. Date: 01 August, 2001 List Price(USD): $58.00 |
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Title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini, John Shawe-Taylor ISBN: 0521780195 Publisher: Cambridge University Press Pub. Date: 23 March, 2000 List Price(USD): $53.00 |
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Title: Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, David G. Stork ISBN: 0471056693 Publisher: Wiley-Interscience Pub. Date: October, 2000 List Price(USD): $120.00 |
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Title: Classification and Regression Trees by Leo Breiman ISBN: 0412048418 Publisher: Kluwer Academic Publishers Pub. Date: 01 January, 1984 List Price(USD): $54.95 |
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Title: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Ian H. Witten, Eibe Frank ISBN: 1558605525 Publisher: Morgan Kaufmann Pub. Date: 11 October, 1999 List Price(USD): $49.95 |
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