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00681nam ac200229 k 4500
000003816037
20220101120000
ta
030203s2001 gw 000 eng
▼a 0387952845
▼a 123456
▼c 123456
▼d 211070
▼l WM2644
▼a Q325.75
▼a Q325.75
▼b F75
▼a Trevor,Hastie
▼a (The)Elements of Statistical Learning : data mining, inference and prediction/
▼d Hastie,Trevor;
▼e Tibshirani,Robert;
▼e Friedman,Jerome
▼a Berlin:
▼b Springer,
▼c 2001.
▼a 533p.;
▼c 25cm.
▼a Supervised learning(Machine learning)
▼a Robert,Tibshirani
▼a Jerome ,Friedman
▼b EUR79
▼a 단행본
| 자료유형 : | 단행본 |
|---|---|
| ISBN : | 0387952845 |
| 분류기호 : | Q325.75 |
| 개인저자 : | Trevor,Hastie |
| 서명/저자사항 : | (The)Elements of Statistical Learning : data mining, inference and prediction/ Hastie,Trevor; Tibshirani,Robert; Friedman,Jerome |
| 발행사항 : | Berlin: Springer, 2001. |
| 형태사항 : | 533p.; 25cm. |
| 개인저자 : | Robert,Tibshirani |
| 개인저자 : | Jerome ,Friedman |
| 언어 | 영어 |
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 discreminants
13. Prototype methods and nearest-neighbors
14. Unsupervised learning
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