The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman.

By:

Hastie, Trevor

Contributor(s):

Material type:

TextSeries: Springer texts in statistics ; 692Publication details: New York : Springer, 2009.Edition: 2nd edDescription: 745 p. : ill. b&w and col. ; 25 cmContent type:

text

ISBN:

9780387848570

Subject(s):

DDC classification:

519.5

Contents:

Ch. 1. Introduction -- Ch. 2. Overview of supervised learning -- Ch. 3. Linear method for regression -- Ch. 4. Linear methods for classification -- Ch. 5. Basis expansions and regularization -- Ch. 6 Kernel smoothing methods -- Ch. 7. Model assessment and selection -- Ch. 8. Model inference and averaging -- Ch. 9. Additive model, trees and related methods -- Ch. 10. Boosting and additive trees -- Ch. 11. Neural networks -- Ch. 12. Support vector machines and flexible discriminants -- Ch. 13. Prototype methods and nearest-neighbors -- Ch. 14. Unsupervised learning -- Ch. 15. Random forests -- Ch. 16. Ensemble learning -- Ch. 17. Undirected graphical models -- Ch. 18. High-dimensional problems: p >> N.

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 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.

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Holdings
Item type	Current library	Call number	Status	Barcode
Books	Marbella International University Centre Library	519.5 HAS ele (Browse shelf(Opens below))	Available	11795

Includes bibliographical references (p. [699]-727) and indexes.

Ch. 1. Introduction --
Ch. 2. Overview of supervised learning --
Ch. 3. Linear method for regression --
Ch. 4. Linear methods for classification --
Ch. 5. Basis expansions and regularization --
Ch. 6 Kernel smoothing methods --
Ch. 7. Model assessment and selection --
Ch. 8. Model inference and averaging --
Ch. 9. Additive model, trees and related methods --
Ch. 10. Boosting and additive trees --
Ch. 11. Neural networks --
Ch. 12. Support vector machines and flexible discriminants --
Ch. 13. Prototype methods and nearest-neighbors --
Ch. 14. Unsupervised learning --
Ch. 15. Random forests --
Ch. 16. Ensemble learning --
Ch. 17. Undirected graphical models --
Ch. 18. High-dimensional problems: p >> N.

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.

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