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The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations

Author: Trevor Hastie; Robert Tibshirani; J H Friedman
Publisher: New York : Springer, ©2001.
Series: Springer series in statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Hastie, Trevor.
Elements of statistical learning.
New York : Springer, ©2001
(DLC) 2001031433
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Trevor Hastie; Robert Tibshirani; J H Friedman
ISBN: 9780387216065 0387216065 6610187436 9786610187430
OCLC Number: 133158147
Description: 1 online resource
Contents: Overview of Supervised Learning --
Linear Methods for Regression --
Linear Methods for Classification --
Basic Expansions and Regularization --
Kernel 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 Discriminates --
Prototype Methods and Nearest Neighbors --
Unsupervised Learning.
Series Title: Springer series in statistics.
Responsibility: Trevor Hastie, Robert Tibshirani, Jerome Friedman.

Abstract:

Contains topics that include neural networks, support vector machines, classification trees and boosting. This book also covers graphical models, random forests, ensemble methods, least angle  Read more...

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<http:\/\/www.worldcat.org\/oclc\/133158147<\/a>> # The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Book<\/a>, schema:CreativeWork<\/a>, schema:MediaObject<\/a> ;\u00A0\u00A0\u00A0\nlibrary:oclcnum<\/a> \"133158147<\/span>\" ;\u00A0\u00A0\u00A0\nlibrary:placeOfPublication<\/a> <http:\/\/id.loc.gov\/vocabulary\/countries\/nyu<\/a>> ;\u00A0\u00A0\u00A0\nlibrary:placeOfPublication<\/a> <http:\/\/dbpedia.org\/resource\/New_York_City<\/a>> ; # New York<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/dewey.info\/class\/006.31\/e21\/<\/a>> ;\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/id.worldcat.org\/fast\/1139041<\/a>> ; # Supervised learning (Machine learning)<\/span>\n\u00A0\u00A0\u00A0\nschema:bookFormat<\/a> schema:EBook<\/a> ;\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/viaf.org\/viaf\/85500756<\/a>> ; # Jerome H. Friedman<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/viaf.org\/viaf\/94025833<\/a>> ; # Robert Tibshirani<\/span>\n\u00A0\u00A0\u00A0\nschema:copyrightYear<\/a> \"2001<\/span>\" ;\u00A0\u00A0\u00A0\nschema:creator<\/a> <http:\/\/viaf.org\/viaf\/85500739<\/a>> ; # Trevor Hastie<\/span>\n\u00A0\u00A0\u00A0\nschema:datePublished<\/a> \"2001<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basic Expansions and Regularization -- Kernel 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 Discriminates -- Prototype Methods and Nearest Neighbors -- Unsupervised Learning.<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"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 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. 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<http:\/\/viaf.org\/viaf\/85500739<\/a>> # Trevor Hastie<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Hastie<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Trevor<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Trevor Hastie<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
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