The Elements of Statistical Learning

Data Mining, Inference, and Prediction

Authors:

ISBN: 978-0-387-84857-0 (Print) 978-0-387-84858-7 (Online)

Table of contents (18 chapters)

  1. Front Matter

    Pages i-xxii

  2. No Access

    Book Chapter

    Pages 1-8

    Introduction

  3. No Access

    Book Chapter

    Pages 9-41

    Overview of Supervised Learning

  4. No Access

    Book Chapter

    Pages 43-99

    Linear Methods for Regression

  5. No Access

    Book Chapter

    Pages 101-137

    Linear Methods for Classification

  6. No Access

    Book Chapter

    Pages 139-189

    Basis Expansions and Regularization

  7. No Access

    Book Chapter

    Pages 191-218

    Kernel Smoothing Methods

  8. No Access

    Book Chapter

    Pages 219-259

    Model Assessment and Selection

  9. No Access

    Book Chapter

    Pages 261-294

    Model Inference and Averaging

  10. No Access

    Book Chapter

    Pages 295-336

    Additive Models, Trees, and Related Methods

  11. No Access

    Book Chapter

    Pages 337-387

    Boosting and Additive Trees

  12. No Access

    Book Chapter

    Pages 389-416

    Neural Networks

  13. No Access

    Book Chapter

    Pages 417-458

    Support Vector Machines and Flexible Discriminants

  14. No Access

    Book Chapter

    Pages 459-483

    Prototype Methods and Nearest-Neighbors

  15. No Access

    Book Chapter

    Pages 485-585

    Unsupervised Learning

  16. No Access

    Book Chapter

    Pages 587-604

    Random Forests

  17. No Access

    Book Chapter

    Pages 605-624

    Ensemble Learning

  18. No Access

    Book Chapter

    Pages 625-648

    Undirected Graphical Models

  19. No Access

    Book Chapter

    Pages 649-698

    High-Dimensional Problems: pN

  20. Back Matter

    Pages 699-745