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The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer Series in Statistics) (Hardcover)

by Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author)

5.0 out of 5 stars See all reviews (1 customer review)

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Hardcover (2nd ed. 2009. Corr. 3rd printing)	�62.99	�51.88	20 used & new from �42.06

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Product details

Hardcover: 552 pages
Publisher: Springer; 1st ed. 2001. Corr. 3rd printing edition (2 Sep 2003)
Language English
ISBN-10: 0387952845
ISBN-13: 978-0387952840
Product Dimensions: 23.8 x 15.6 x 3 cm

Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)

Amazon.co.uk Sales Rank: 251,328 in Books (See Bestsellers in Books)

Popular in these categories:

#10 in	Books > Computing & Internet > Databases > Data Storage & Management > Data Mining
#21 in	Books > Computing & Internet > Computer Science > Algorithms > Machine Learning
#24 in	Books > Computing & Internet > Computer Science > Artificial Intelligence > Machine Learning

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Product Description

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

About the Author

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

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	82% buy the item featured on this page: The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer Series in Statistics) 5.0 out of 5 stars (1)
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8 of 11 people found the following review helpful:

5.0 out of 5 stars An excellent statistical interpretation of Data Mining, 28 Sep 2001

By A Customer

The book provides a long-sought link between Statistics and Data Mining. Problems of classification are adequately addressed with regard to both model accuracy and reliability. The discussion of boosting and its evolution demonstrates just how fast "greediness" for accurate classifiers is growing.

In the years to come will apparently witness an increasing wave of multi-disciplinary approaches in devising and modifying classification techniques. Whenever that comes, this book will already have made its contribution to that development.

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