Hastie, Trevor

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]

006.31