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The Elements of Statistical Learning : Data Mining, Inference, and Prediction, Second Edition
by Trevor Hastie and Robert Tibshirani and Jerome Friedman

Overview - * The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book * Includes more than 200 pages of four-color graphics During the past decade there has been an explosion in computation and information technology.  Read more...

 
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More About The Elements of Statistical Learning by Trevor Hastie; Robert Tibshirani; Jerome Friedman
 
 
 
Overview
* The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book * Includes more than 200 pages of four-color graphics 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. 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-develope


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Details
  • ISBN-13: 9780387848570
  • ISBN-10: 0387848576
  • Publisher: Springer
  • Publish Date: December 2008
  • Page Count: 745

Series: Springer Series in Statistics

Related Categories

Books > Mathematics > Probability & Statistics - General
Books > Computers & Internet > Databases - Data Mining
Books > Computers & Internet > Intelligence (AI) & Semantics

 
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