000			01282nam a2200241Ia 4500
003			OSt
005			20220302162428.0
008			220128s9999 xx 000 0 und d
020			_a9780387848570
040			_cCUS
082			_22nd ed. _a006.3122 _bHAS/E
100			_aHastie, Trevor _92989
245		4	_aThe Elements of Statistical Learning: Data Mining, Inference, and Prediction
260			_aNew YORK: _bSpringer, _c2017
300			_axxii, 745p.
505			_aContenido: 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.
650			_aArtificial intelligence Bioinformatics Data mining _94383
650			_aElectronic data processing _94384
700			_aTibshirani, Robert _94385
700			_aFriedman, Jerome _94386
942			_2ddc _cWB16
947			_a3457
999			_c211231 _d211231