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024 7 _a10.1007/978-0-387-84858-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
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_223
100 1 _aHastie, Trevor.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_931615
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.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2009.
300 _aXXII, 745 p. 658 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Statistics,
_x2197-568X
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.
_93407
650 0 _aData mining.
_93907
650 0 _aProbabilities.
_94604
650 0 _aStatistics .
_931616
650 0 _aBioinformatics.
_99561
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aData Mining and Knowledge Discovery.
_931617
650 2 4 _aProbability Theory.
_917950
650 2 4 _aStatistical Theory and Methods.
_931618
650 2 4 _aComputational and Systems Biology.
_931619
700 1 _aTibshirani, Robert.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_931620
700 1 _aFriedman, Jerome.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_931621
710 2 _aSpringerLink (Online service)
_931622
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9780387848846
776 0 8 _iPrinted edition:
_z9780387848570
776 0 8 _iPrinted edition:
_z9781071621226
830 0 _aSpringer Series in Statistics,
_x2197-568X
_931623
856 4 0 _uhttps://doi.org/10.1007/978-0-387-84858-7
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