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The elements of statistical learning: Data Mining, Inference, and Prediction
"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 th...
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| 其他作者: | , |
| 格式: | Printed Book |
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New York, NY :
Springer,
c2009.
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| 版: | 2nd ed. |
| 叢編: | Springer series in statistics,
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| 主題: |
書本目錄:
- 1. Introduction
- 2. Overview of supervised learning
- 3. Linear methods for regression
- 4. Linear methods for classification
- 5. Basis expansions and regularization
- 6. Kernel smoothing 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
- 15. Random forests
- 16. Ensemble learning
- 17. Undirected graphical models
- 18. High-dimensional problems: p>> N.