JOURNAL OF CLASSIFICATION, JUNE 2004
"This is a great book. All three authors have track records for clear exposition and are famously gifted for finding intuitive explanations that illuminate technical results…In particular, we admire the book for its:
-outstanding use of real data examples to motivate problems and methods;
-unified treatment of flexible inferential procedures in terms of maximization of an objective function subject to a complexity penalty;
-lucid explanation of the amazing performance of the AdaBoost algorithm in improving classification accuracy for almost any rule;
-clear account of support vector machines in terms of traditional statistical paradigms;
-regular introduction of some new insight, such as describing self-organizing maps as constrained k-means clustering.
…No modern statistician or computer scientist should be without this book."
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, JUNE 2004
"In the words of the authors, the goal of this book was to ‘bring together many of the important new ideas in learning, and explain them in a statistical framework.’ The authors have been quite successful in achieving this objective, and their work is a welcome addition to the statistics and learning literatures…A strength of the book is the attempt to organize a plethora of methods into a coherent whole. The relationships among the methods are emphasized. I know of no other book that covers so much ground."
Customer Reviews & Comments
I use data mining tools in my financial engineering and financial modeling work and I have found this book to be very useful. This book provides two crucial types of information. First, it provides enough theory to allow a potential user to understand the essential insights that motivate specific techniques and to evaluate the situations in which those technique are appropriate. Second, the book gives the exact algorithms to implement the various techniques. While no book I have seen covers every data mining methodology available, this one has the strongest coverage I have seen in additive models, non-linear regression, and CART/MART (regression/classification trees). It also has very strong coverage in many other areas. I highly recommend it.
