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- catalog abstract "Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.".
- catalog contributor b12708522.
- catalog contributor b12708523.
- catalog contributor b12708524.
- catalog created "c2001.".
- catalog date "2001".
- catalog date "c2001.".
- catalog dateCopyrighted "c2001.".
- catalog description "Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.".
- catalog description "Includes bibliographical references (p. [509]-522) and indexes.".
- catalog description "Introduction -- Overview of supervised learning -- Linear methods of regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel methods -- Model assessment and selection -- Model interference and averaging -- Additive methods, trees and related methods -- Boosting and additive trees -- Neural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest-neighbors -- Unsupervised learning".
- catalog extent "xvi, 533 p. :".
- catalog identifier "0387952845 (alk. paper)".
- catalog isPartOf "Springer series in statistics".
- catalog issued "2001".
- catalog issued "c2001.".
- catalog language "eng".
- catalog publisher "New York : Springer,".
- catalog subject "006.3/1 21".
- catalog subject "Computational Biology methods.".
- catalog subject "Data Interpretation, Statistical.".
- catalog subject "Q325.75 .H37 2001".
- catalog subject "Statistics as Topic methods.".
- catalog subject "Supervised learning (Machine learning)".
- catalog tableOfContents "Introduction -- Overview of supervised learning -- Linear methods of regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel methods -- Model assessment and selection -- Model interference and averaging -- Additive methods, trees and related methods -- Boosting and additive trees -- Neural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest-neighbors -- Unsupervised learning".
- catalog title "The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman.".
- catalog type "text".