The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Front Cover
Springer Science & Business Media, 2001 - Mathematics - 533 pages
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 apopular 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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I own the 2nd edition of this book. The topics are described more from a statistics perspective than the computer science perspective, but as written by statisticians for computer scientists instead of for other statisticians. The examples are interesting and the graphics very nice.

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Page 513 - Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, Morgan Kauffman, San Francisco, pp.
Page 513 - Proceedings of the Ninth Annual Conference on Computational Learning Theory, pp. 325-332. Freund, Y. and Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences 55: 119-139.
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Page 514 - Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis. London: Chapman & Hall.
Page 517 - Kohonen, T. (1989). Self-Organization and Associative Memory (3rd edition), Springer- Verlag, Berlin.
Page 509 - AR ( 1993 | Universal approximation bounds for superpositions of a sigmoid function. IEEE Transactions on Information Theory 39.
Page 509 - JA and Rosenfeld, E. (eds) (1988). Neurocomputing: Foundations of Research. MIT Press : Cambridge, MA.
Page 518 - Madigan, D. and Raftery, A. (1994), Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window.

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