The elements of statistical learning : data mining, inference, and prediction /
Trevor Hastie, Robert Tibshirani and Jerome Friedman
- 2a. ed.
- 745 p. il. 2
- Springer series in statistics .
Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. 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 neares-neighbors. Unsupervised learning. Random forests. Ensemble learning. Undirected graphical models. High-dimensional problems.
Sistemas
9780387848570
APRENDIZAJE AUTOMATICO COMPUTADORES DIGITALES INTELIGENCIA ARTIFICIAL METODOLOGIA EN ESTADISTICA PROCESAMIENTO ELECTRONICO DE DATOS PROGRAMACION [MATEMATICAS] REDES NEURONALES [COMPUTADORES]