TY  - DATA
AU  - Hastie, Trevor
AU  - Tibshirani, Robert
AU  - Friedman, J. H.
TI  - The elements of statistical learning: data mining, inference, and prediction 
SN  - 9780387848587 (E-book)
AV  - Q 325.5 H37E 2009
PY  - 2009///
CY  - New York,
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 index; Introduction -- 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: p ≫ N
UR  - https://drive.google.com/file/d/1cS9-MrVnAYrSKjEGrGQRSd1fqT0uW1J2/view?usp=sharing
ER  -