countdown boutiques-francophones Beauty home Kindle Explore the Vinyl LP Records Store sports Tools

There was a problem filtering reviews right now. Please try again later.

on July 11, 2017
Great value and price for this book. Only caveat is that it came in with rough corners, and had a kind of dirty (sticky?) back cover. The mailing process does not seem to be responsible for this, as the book was well packed. It does not affect the actual content of the book, all pages are in great condition. Thank you!
0Comment|Was this review helpful to you?YesNoReport abuse
on November 10, 2015
Amazing book.
0Comment|Was this review helpful to you?YesNoReport abuse
on August 10, 2017
Bad quality.
0Comment|Was this review helpful to you?YesNoReport abuse
on May 7, 2016
It's very good condition.
0Comment|Was this review helpful to you?YesNoReport abuse
on March 14, 2004
I was already familiar with many of the topics covered in this book, but had to do a double take when reading about familiar concepts. Unfortunately, the authors' unique perspective is not presented in a way that is benificial to the reader. I would strongly suggest another book as a reference or introduction to this material.
0Comment| One person found this helpful. Was this review helpful to you?YesNoReport abuse
on October 16, 2011
Probably the best book on machine learning. I use it mostly as a reference book, as it deals with a wide range of statistical learning methods, from basic ones to state of the art.

Perhaps not as good when used as a coursebook, though. Beginners will find that the difficulty of the text varies, even from paragraph to paragraph.
0Comment| 2 people found this helpful. Was this review helpful to you?YesNoReport abuse
on September 11, 2003
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.
0Comment| 2 people found this helpful. Was this review helpful to you?YesNoReport abuse
VINE VOICEon November 26, 2015
The author is talking to himself! No intended audience group. Just the author makes it clear that he understands the material, while the reader tries to catch on.

Worst book ever written in statistics.
0Comment|Was this review helpful to you?YesNoReport abuse
on June 17, 2004
This book is a miracle of clarity and comprehensiveness. It presents a unified approach to state of the art machine learning techniques from a statistical perspective. The layout is logical and the level of math is appropriate for applications-oriented engineers and computer scientists, as well as theorists. Sections where the book does need to go into heavier mathematics are clearly marked and generally optional. I found the book very easy to read, but at the same time very comprehensive.
The book provides a very illuminating counterpoint to other books that promote the Computational Learning Theory (COLT / kernels / large margins) viewpoint of modern machine learning. Many of the same techniques such as boosting and support vector machines are discussed, but are motivated in different ways. Appropriate regularization is seen as the key to avoiding overfitting with complex models, rather than margin maximization. Mathematically you often end up solving the same problem, but personally I usually find the statistical approach much more direct and intuitive.
This book is a nice follow on to introductory pattern recognition texts such as Duda and Hart, though it can be read without any prior pattern recognition knowledge. It provides a nice mix of theory and paractical algorithms, illustrated with numerous examples. An essential element of your machine learning library!
0Comment|Was this review helpful to you?YesNoReport abuse
on October 1, 2001
Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.
Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.
These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.
The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.
The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.
Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.
This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. The only comparable text is the text by Mannila, Hand and Smyth that I hope to be able to review in the near future.
0Comment|Was this review helpful to you?YesNoReport abuse