The elements of statistical learning : data mining, inference, and prediction /
Uloženo v:
| Hlavní autoři: | , , |
|---|---|
| Typ dokumentu: | Kniha |
| Jazyk: | Angličtina |
| Vydáno: |
New York :
Springer,
[2009]
|
| Vydání: | Second edition |
| Edice: | Springer series in statistics,
|
| Témata: | |
| On-line přístup: | Elektronická verze přístupná pouze pro studenty a pracovníky MU |
| Příbuzné jednotky: | Tištěná verze::
Elements of statistical learning. |
Obsah:
- 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.