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

Main Author Hastie, Trevor, 1953- Coauthor Tibshirani, Robert
Friedman, J. H.
Country Estados Unidos. Publication New York : Springer, cop. 2001 Description XVI, 533 p. : il. ; 25 cm Series Springer series in statistics ISBN 0-387-95284-5 CDU 519.2
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Item type Current location Call number Status Date due Barcode Item holds
Monografia Biblioteca da UMinho no Campus de Azurém
BPG2 519.2 - H Available 322651
Monografia Biblioteca da UMinho no Campus de Azurém
BPG2 519.2 - H Available 328794
Monografia Biblioteca Geral da Universidade do Minho
BGUMD 108993 Available 331890
Monografia Biblioteca da UMinho no Campus de Azurém
BPG 519.2 - H Available 353341
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Enhanced descriptions from Syndetics:

This book describes the important ideas 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 should be a valuable resource for statisticians and anyone interested in data mining in science or industry.

Table of contents provided by Syndetics

  • Introduction
  • Overview of Supervised Larnings
  • 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 Discriminants
  • Prototype Methods and Nearest Neighbors
  • Unsupervised Learning

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