Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Executive Summary
- 1 Rudiments of Statistical Learning Theory
- 2 Vapnik–Chervonenkis Dimension
- 3 Learnability for Binary Classification
- 4 Support Vector Machines
- 5 Reproducing Kernel Hilbert Spaces
- 6 Regression and Regularization
- 7 Clustering
- 8 Dimension Reduction
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
1 - Rudiments of Statistical Learning Theory
from Part One - Machine Learning
Published online by Cambridge University Press: 21 April 2022
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Executive Summary
- 1 Rudiments of Statistical Learning Theory
- 2 Vapnik–Chervonenkis Dimension
- 3 Learnability for Binary Classification
- 4 Support Vector Machines
- 5 Reproducing Kernel Hilbert Spaces
- 6 Regression and Regularization
- 7 Clustering
- 8 Dimension Reduction
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
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Summary
This first chapter introduces the key concepts of statistical learning theory, such as generalization error, empirical risk minimization, bias-complexity tradeoff, and validation. It also describes the probably approximately correct (PAC) framework and establishes that finite hypothesis classes are PAC-learnable.
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- Publisher: Cambridge University PressPrint publication year: 2022
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