文件名称：The Elements of Statistical Learning
文件大小：13.16MB
文件格式：PDF
更新时间：2022-03-29 07:49:57
statis deep l
the second edition chapter: 1. Introduction 2. Overview of Supervised Learning 3. Linear Methods for Regression 4. Linear Methods for Classification 5. Basis Expansions and Regulariza- tion 6. Kernel Smoothing Methods 7. Model Assessment and Selection 8. Model Inference and Averaging 9. Additive Models, Trees, and Related Methods 10. Boosting and Additive Trees 11. Neural Networks 12. Support Vector Machines and Flexible Discriminants 13. Prototype Methods Nearest-Neighbors 14. Unsupervised Learning 15. Random Forests 16. Ensemble Learning 17. Undirected Graphical Models 18. High-Dimensional Problems