000			04614nam a22005055i 4500
999			_c17994 _d17994
001			978-0-387-84858-7
003			DE-He213
005			20200712171050.0
007			cr nn 008mamaa
008			100301s2009 xxu| s |||| 0|eng d
020			_a9780387848587
024	7		_a10.1007/978-0-387-84858-7 _2doi
040			_cBT-SaRUSC
082	0	4	_a006.3 _223 _bHAS
100	1		_aHastie, Trevor. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	4	_aThe Elements of Statistical Learning _h[electronic resource] : _bData Mining, Inference, and Prediction, Second Edition / _cby Trevor Hastie, Robert Tibshirani, Jerome Friedman.
250			_a2nd ed. 2009.
300			_aXXII, 745 p. 658 illus. _bonline resource. _g610 MB
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aOverview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basis Expansions and Regularization -- Kernel Smoothing Methods -- Model Assessment and Selection -- Model Inference and Averaging -- Additive Models, Trees, and Related Methods -- Boosting and Additive Trees -- Neural Networks -- Support Vector Machines and Flexible Discriminants -- Prototype Methods and Nearest-Neighbors -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- High-Dimensional Problems: p ? N.
520			_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.
650		0	_aArtificial intelligence.
650		0	_aData mining.
650		0	_aProbabilities.
650		0	_aStatistics .
650		0	_aBioinformatics.
650		0	_aBioinformatics .
650		0	_aComputational biology .
650	1	4	_aArtificial Intelligence.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aComputational Biology/Bioinformatics.
650	2	4	_aComputer Appl. in Life Sciences.
700	1		_aTibshirani, Robert. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aFriedman, Jerome. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387848846
776	0	8	_iPrinted edition: _z9780387848570
830		0	_aSpringer Series in Statistics,
856	4	0	_uhttps://doi.org/10.1007/978-0-387-84858-7
856	4	0	_uhttp://library.sce.edu.bt/cgi-bin/koha/opac-retrieve-file.pl?id=18922b2800ef6c777a9a6d3dbad357b0 _yEbook
942			_2ddc _cCF