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  E-mail Message: I thought you might be interested in this item at http://www.worldcat.org/oclc/1058138445 Title: The elements of statistical learning : data mining, inference and prediction Author: Trevor Hastie; Robert Tibshirani; Jerome Friedman Publisher: New York, N.Y. : Springer, 2009. ISBN/ISSN: 9780387848570 0387848576 9780387848587 0387848584 9781282827264 128282726X 1282126741 9781282126749 9786612126741 6612126744 OCLC:1058138445

  
  

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The elements of statistical learning : data mining, inference and prediction

Author:	Trevor Hastie; Robert Tibshirani; Jerome Friedman
Publisher:	New York, N.Y. : Springer, 2009.
Series:	Springer series in statistics.
Edition/Format:	eBook : Document : English : 2nd editionView all editions and formats
Summary:	"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 is 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 and 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."--Publisher's website.  Read more...
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Subjects	Machine learning. Statistics -- Methodology. Bioinformatics. View all subjects
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