Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Basic Probability Inequalities for Sums of Independent Random Variables
- 3 Uniform Convergence and Generalization Analysis
- 4 Empirical Covering Number Analysis and Symmetrization
- 5 Covering Number Estimates
- 6 Rademacher Complexity and Concentration Inequalities
- 7 Algorithmic Stability Analysis
- 8 Model Selection
- 9 Analysis of Kernel Methods
- 10 Additive and Sparse Models
- 11 Analysis of Neural Networks
- 12 Lower Bounds and Minimax Analysis
- 13 Probability Inequalities for Sequential Random Variables
- 14 Basic Concepts of Online Learning
- 15 Online Aggregation and Second-Order Algorithms
- 16 Multiarmed Bandits
- 17 Contextual Bandits
- 18 Reinforcement Learning
- Appendix A Basics of Convex Analysis
- Appendix B f-divergence of Probability Measures
- References
- Author Index
- Subject Index
14 - Basic Concepts of Online Learning
Published online by Cambridge University Press: 20 July 2023
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Basic Probability Inequalities for Sums of Independent Random Variables
- 3 Uniform Convergence and Generalization Analysis
- 4 Empirical Covering Number Analysis and Symmetrization
- 5 Covering Number Estimates
- 6 Rademacher Complexity and Concentration Inequalities
- 7 Algorithmic Stability Analysis
- 8 Model Selection
- 9 Analysis of Kernel Methods
- 10 Additive and Sparse Models
- 11 Analysis of Neural Networks
- 12 Lower Bounds and Minimax Analysis
- 13 Probability Inequalities for Sequential Random Variables
- 14 Basic Concepts of Online Learning
- 15 Online Aggregation and Second-Order Algorithms
- 16 Multiarmed Bandits
- 17 Contextual Bandits
- 18 Reinforcement Learning
- Appendix A Basics of Convex Analysis
- Appendix B f-divergence of Probability Measures
- References
- Author Index
- Subject Index
Summary
In online learning, we consider a learning model that is different from that of supervised learning, in that we make predictions sequentially and obtain feedback after predictions are made. In this chapter, we introduce this learning model as well as some first-order online learning algorithms.
- Type
- Chapter
- Information
- Mathematical Analysis of Machine Learning Algorithms , pp. 289 - 306Publisher: Cambridge University PressPrint publication year: 2023