000			03216nam a22003735i 4500
001			64757
005			20240104063428.0
010			_a978-0-387-84858-7 _dcompra
090			_a64757
100			_a20150401d2009 k||y0pory50 ba
101			_aeng
102			_aUS
200			_aThe elements of statistical learning _bDocumento electrónico _edata mining, inference, and prediction _fTrevor Hastie, Robert Tibshirani, Jerome Friedman
210			_aNew York _cSpringer _d2009
215			_aXXII, 745 p. _cil.
225			_aSpringer Series in Statistics
300			_aColocação: Online
303			_aDuring 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-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.
606			_aMachine learning
606			_aArmazenamento de dados
606			_aBioinformática
606			_aInferência
606			_aEstatística
606			_aInteligência artificial
680			_aQ325.5
700			_aHastie _bTrevor
701			_aTibshirani _bRobert J. _4070 _98282
701			_aFriedman _bJerome _4070 _935638
801			_aPT _gRPC
856			_uhttp://dx.doi.org/10.1007/978-0-387-84858-7
942			_2lcc _cF _n0