Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- Part 1 Foundations
- Part 2 From Theory to Algorithms
- Part 3 Additional Learning Models
- Part 4 Advanced Theory
- 26 Rademacher Complexities
- 27 Covering Numbers
- 28 Proof of the Fundamental Theorem of Learning Theory
- 29 Multiclass Learnability
- 30 Compression Bounds
- 31 PAC-Bayes
- Appendix A Technical Lemmas
- Appendix B Measure Concentration
- Appendix C Linear Algebra
- References
- Index
31 - PAC-Bayes
from Part 4 - Advanced Theory
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
- Part 3 Additional Learning Models
- Part 4 Advanced Theory
- 26 Rademacher Complexities
- 27 Covering Numbers
- 28 Proof of the Fundamental Theorem of Learning Theory
- 29 Multiclass Learnability
- 30 Compression Bounds
- 31 PAC-Bayes
- 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. 364 - 368Publisher: Cambridge University PressPrint publication year: 2014