| 000 | 01680 a2200289 4500 | ||
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| 999 |
_c2339 _d2339 |
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| 005 | 20240613150830.0 | ||
| 008 | 240613b ||||| |||| 00| 0 eng d | ||
| 020 | _a9780387848570 | ||
| 041 | _aeng | ||
| 082 | _a006.3 HAS/E | ||
| 100 |
_aHastie, Trevor _91087 |
||
| 245 | _aThe elements of statistical learning: data mining, inference, and prediction. | ||
| 250 | _a2nd ed. | ||
| 260 |
_bSpringer -- _c2009 _aUnited States of America -- |
||
| 300 | _axxii, 745p. | ||
| 500 | _a* Overview of supervised learning * Linear methods for regression * Linear methods for classification * Basis expansions and regularization * Kernel smoothing 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 * Random forests * Ensemble learning * Undirected graphical models * High-dimensional problems | ||
| 520 | _a This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world. | ||
| 650 |
_aComputer science _91296 |
||
| 650 |
_aArtificial Intelligence _91643 |
||
| 650 |
_aMachine learning _91055 |
||
| 650 |
_aBioinformatics _98861 |
||
| 650 |
_aForecasting _98862 |
||
| 650 |
_aComputational intelligence _98863 |
||
| 700 |
_aTibshirani, Robert _91088 |
||
| 700 |
_aFriedman, Jerome _94271 |
||
| 942 | _cBK | ||