文件名称：The Elements of Statistical Learning 统计学习精要
文件大小：12.16MB
文件格式：PDF
更新时间：2019-06-29 07:59:40
Data Mining, Inference, and Prediction
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 Regulariza-tion 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