The elements of statistical learning: data mining, inference, and prediction
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.
Read More ↓Title Statements
- Full title :The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2001.
Publication Statements
- Publisher(s):Springer
- Place of Publication:New York
- Language:eng
- Publication Date:2001.
The Physical Object
- Description:xvi, 533 pages : : illustrations (some color) ; : 25 cm
Edition Identifiers
- ISBN:0387952845, 9780387952840
- oclc:ocm51681104
- LC Classification:Q325.75
- Dewey Decimal:006.3/1
Table of Contents
1. Introduction -- 2. Overview of Supervised Learning -- 3. Linear Methods for Regression -- 4. Linear Methods for Classification -- 5. Basis Expansions and Regularization -- 6. Kernel Methods -- 7. Model Assessment and Selection -- 8. Model Inference and Averaging -- 9. Additive Models, Trees, and Related Methods -- 10. Boosting and Additive Trees -- 11. Neural Networks -- 12. Support Vector Machines and Flexible Discriminants -- 13. Prototype Methods and Nearest-Neighbors -- 14. Unsupervised Learning.
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