The
elements of statistical learning
data mining, inference, and prediction
Hastie, Trevor
creator
Friedman, Jerome
Tibshirani, Robert
text
xx
2009
2a. ed.
monographic
eng
745 p. il.
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.
specialized
Trevor Hastie, Robert Tibshirani and Jerome Friedman
Sistemas
xxu
APRENDIZAJE AUTOMATICO
COMPUTADORES DIGITALES
INTELIGENCIA ARTIFICIAL
METODOLOGIA EN ESTADISTICA
PROCESAMIENTO ELECTRONICO DE DATOS
PROGRAMACION [MATEMATICAS]
REDES NEURONALES [COMPUTADORES]
006.31
9780387848570
Sistemas
140217
20230801164313.0
014767