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The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman

By: Hastie, Trevor [aut. ].
Contributor(s): Tibshirani, Robert [aut. ] | Friedman, J. H [aut. ].
Material type: materialTypeLabelBookSeries: Springer series in statistics: Publisher: New York, NY : Springer, c2009Edition: 2a ed.Description: xxii, 745 p. : ill. (some col.) ; 25 cm.ISBN: 9780387848570 (hardcover : alk. paper); 9780387848587 (electronic).Subject(s): Aprenentatge automàtic | Estadística -- Metodologia | Mineria de dades | Bioinformàtica | Previsió | Intel·ligència computacionalMateria (POPIN): ESTADÍSTICA | METODOLOGÍA | ANÁLISIS DE REGRESIÓN | MÉTODOS ESTADÍSTICOS | MINERÍA DE DATOSMatèria (POPIN): ESTADÍSTICA | METODOLOGIA | ANÀLISI DE REGRESSIÓ | MÈTODES ESTADÍSTICS | MINERIA DE DADESSubject (POPIN): STATISTICS | METHODOLOGY | REGRESSION ANALYSIS | STATISTICAL METHODS | DATA MININGSummary: This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

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