Friedman, Jerome
The Elements of Statistical Learning
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regularization
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 and Nearest-Neighbors
14. Unsupervised Learning
15. Random Forests
16. Ensemble Learning
17. Undirected Graphical Models
18. High-Dimensional Problems: p N
Keywords: Statistics, Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences, Computer Appl. in Life Sciences, Artificial Intelligence (incl. Robotics), Computational Biology/Bioinformatics, Data Mining and Knowledge Discovery, Statistical Theory and Methods
- Author(s)
- Friedman, Jerome
- Hastie, Trevor
- Tibshirani, Robert
- Publisher
- Springer
- Publication year
- 2009
- Language
- en
- Edition
- 1
- Series
- Springer Series in Statistics
- Page amount
- 768 pages
- Category
- Natural Sciences
- Format
- Ebook
- eISBN (PDF)
- 9780387848587