Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part one Pattern Classification with Binary-Output Neural Networks
- Part two Pattern Classification with Real-Output Networks
- Part three Learning Real-Valued Functions
- Part four Algorithmics
- 22 Efficient Learning
- 23 Learning as Optimization
- 24 The Boolean Perceptron
- 25 Hardness Results for Feed-Forward Networks
- 26 Constructive Learning Algorithms for Two-Layer Networks
- Appendix 1 Useful Results
- Bibliography
- Author index
- Subject index
24 - The Boolean Perceptron
Published online by Cambridge University Press: 26 February 2010
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part one Pattern Classification with Binary-Output Neural Networks
- Part two Pattern Classification with Real-Output Networks
- Part three Learning Real-Valued Functions
- Part four Algorithmics
- 22 Efficient Learning
- 23 Learning as Optimization
- 24 The Boolean Perceptron
- 25 Hardness Results for Feed-Forward Networks
- 26 Constructive Learning Algorithms for Two-Layer Networks
- Appendix 1 Useful Results
- Bibliography
- Author index
- Subject index
- Type
- Chapter
- Information
- Neural Network LearningTheoretical Foundations, pp. 316 - 330Publisher: Cambridge University PressPrint publication year: 1999