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

Nyckelord: 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