TY  - BOOK
AU  - James Gareth
AU  -  Daniela Witten 
AU  -  Trevor Hastie
AU  -  Robert Tibshirani
TI  - An introduction to statistical learning : with applications in R
T2  -  Springer Texts in Statistics
SN  - 9781461471370
AV  - QA 276 
PY  - 2013///]
CY  - New York
PB  - Springer
KW  - Statistics
N1  - Ch 1. Introduction
Ch 2. Statistical Learning
Ch 3. Linear Regression
Ch 4. Classification
Ch 5. Resampling Methods
Ch 6. Linear Model Selection and Regularization
Ch 7. Moving Beyond Linearity 
Ch 8. Tree-Based Methods
Ch 9. Support Vector Machines
Ch 10. Unsupervised Learning
N2  - An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. --- summary provided by publisher
ER  -