Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part one Pattern Classification with Binary-Output Neural Networks
- Part two Pattern Classification with Real-Output Networks
- 9 Classification with Real-Valued Functions
- 10 Covering Numbers and Uniform Convergence
- 11 The Pseudo-Dimension and Fat-Shattering Dimension
- 12 Bounding Covering Numbers with Dimensions
- 13 The Sample Complexity of Classification Learning
- 14 The Dimensions of Neural Networks
- 15 Model Selection
- Part three Learning Real-Valued Functions
- Part four Algorithmics
- Appendix 1 Useful Results
- Bibliography
- Author index
- Subject index
12 - Bounding Covering Numbers with Dimensions
Published online by Cambridge University Press: 26 February 2010
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part one Pattern Classification with Binary-Output Neural Networks
- Part two Pattern Classification with Real-Output Networks
- 9 Classification with Real-Valued Functions
- 10 Covering Numbers and Uniform Convergence
- 11 The Pseudo-Dimension and Fat-Shattering Dimension
- 12 Bounding Covering Numbers with Dimensions
- 13 The Sample Complexity of Classification Learning
- 14 The Dimensions of Neural Networks
- 15 Model Selection
- Part three Learning Real-Valued Functions
- Part four Algorithmics
- Appendix 1 Useful Results
- Bibliography
- Author index
- Subject index
- Type
- Chapter
- Information
- Neural Network LearningTheoretical Foundations, pp. 165 - 183Publisher: Cambridge University PressPrint publication year: 1999