The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- New YORK: Springer, 2017
- xxii, 745p.
Contenido: 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.
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Artificial intelligence Bioinformatics Data mining Electronic data processing