TY  - BOOK
AU  - Hastie,Trevor
AU  - Tibshirani,Robert
AU  - Friedman,J.H.
TI  - The elements of statistical learning: data mining, inference, and prediction 
T2  - Springer series in statistics,
SN  - 9780387848570
AV  - Q325.75 .H37 2009
U1  - 006.3/1 22
PY  - 2017///
CY  - New York, NY
PB  - Springer
KW  - Machine learning
KW  - Statistics
KW  - Methodology
KW  - Data mining
KW  - Bioinformatics
KW  - Inference
KW  - Forecasting
KW  - Computational intelligence
N1  - Includes bibliographical references (p. [699]-727) and indexes; Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basic expansions and regularization -- Kernel smoothing methods -- Mode;s assessment and selection -- Model assessment and selection -- Additive models,trees , and related methods --Boosting and additive trees -- Natural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest- neighbors Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphic models  - High- dimensional problems : p>>n
ER  -