文件名称:
The Elements of Statistical Learning Data Mining Inference and Prediction
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文件大小: 8mb
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上传时间: 2009-11-29
详细说明： 机器学方面一本很新， 很全面的书。 pdf超好。 彩版的。 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- Additional illustrations of RKHS tion 6. Kernel Smoothing Methods 7. Model Assessment and Selection Strengths and pitfalls of cross- valida tion 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 Path algorithm for SVM classifier Flexible Discriminants 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 ...展开收缩
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