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The Elements of Statistical Learning - Hastie, Tibshirani, Friedman

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 							The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition)
  
 
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
  ISBN: 978-0387848570
  File
format: PDF
  
  
  
  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 & 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.
   						

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