Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- Part 1 Foundations
- Part 2 From Theory to Algorithms
- 9 Linear Predictors
- 10 Boosting
- 11 Model Selection and Validation
- 12 Convex Learning Problems
- 13 Regularization and Stability
- 14 Stochastic Gradient Descent
- 15 Support Vector Machines
- 16 Kernel Methods
- 17 Multiclass, Ranking, and Complex Prediction Problems
- 18 Decision Trees
- 19 Nearest Neighbor
- 20 Neural Networks
- Part 3 Additional Learning Models
- Part 4 Advanced Theory
- Appendix A Technical Lemmas
- Appendix B Measure Concentration
- Appendix C Linear Algebra
- References
- Index
9 - Linear Predictors
from Part 2 - From Theory to Algorithms
Published online by Cambridge University Press: 05 July 2014
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- Part 1 Foundations
- Part 2 From Theory to Algorithms
- 9 Linear Predictors
- 10 Boosting
- 11 Model Selection and Validation
- 12 Convex Learning Problems
- 13 Regularization and Stability
- 14 Stochastic Gradient Descent
- 15 Support Vector Machines
- 16 Kernel Methods
- 17 Multiclass, Ranking, and Complex Prediction Problems
- 18 Decision Trees
- 19 Nearest Neighbor
- 20 Neural Networks
- Part 3 Additional Learning Models
- Part 4 Advanced Theory
- Appendix A Technical Lemmas
- Appendix B Measure Concentration
- Appendix C Linear Algebra
- References
- Index
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- Understanding Machine LearningFrom Theory to Algorithms, pp. 89 - 100Publisher: Cambridge University PressPrint publication year: 2014