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Weiwei Cheng's blog

New book: The Elements of Statistical Learning 2nd Edition

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This is an email message sent to all KEBI members. It might be also of interest to the readers here, so I decide to post it as well.


Hi all,

the 2nd edition of the classic machine learning text book “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedmanis is on the market. There are four brand new chapters and quite a number of changes have been made in the existing chapters. See the list below.

On top of that, the 2nd edition of this book is officially FREE (I hope this email will not be classified as spam due to this word)! It can be downloaded at:  http://www-stat.stanford.edu/~hastie/Papers/ESLII.pdf

Best
Weiwei

Chapter What’s new
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression LAR algorithm and generalizations of the lasso
4. Linear Methods for Classification Lasso path for logistic regression
5. Basis Expansions and Regularization Additional illustrations of RKHS
6. Kernel Smoothing Methods
7. Model Assessment and Selection Strengths and pitfalls of cross-validation
8. Model Inference and Averaging
9. Additive Models, Trees, and Related Methods
10. Boosting and Additive Trees New example from ecology; some material split off to Chapter 16.
11. Neural Networks Bayesian neural nets and the NIPS 2003 challenge
12. Support Vector Machines and Flexible Discriminants Path algorithm for SVM classifier
13. Prototype Methods and Nearest-Neighbors
14. Unsupervised Learning Spectral clustering, kernel PCA, sparse PCA, non-negative matrix factorization archetypal analysis, nonlinear dimension reduction, Google page rank algorithm, a direct approach to ICA
15. Random Forests New
16. Ensemble Learning New
17. Undirected Graphical Models New
18. High-Dimensional Problems New
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Written by Weiwei

29/10/2009 在 18:28

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