Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Executive Summary
- 9 Foundational Results of Optimal Recovery
- 10 Approximability Models
- 11 Ideal Selection of Observation Schemes
- 12 Curse of Dimensionality
- 13 Quasi-Monte Carlo Integration
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
10 - Approximability Models
from Part Two - Optimal Recovery
Published online by Cambridge University Press: 21 April 2022
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Executive Summary
- 9 Foundational Results of Optimal Recovery
- 10 Approximability Models
- 11 Ideal Selection of Observation Schemes
- 12 Curse of Dimensionality
- 13 Quasi-Monte Carlo Integration
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
Summary
In this chapter, the problem of optimal recovery is studied relatively to a model set defined through approximation properties. For the two situations emphasized in the previous chapter, it is shown that the intrinsic errors can be computed exactly and that the linear optimal recovery maps can be efficiently constructed.
- Type
- Chapter
- Information
- Mathematical Pictures at a Data Science Exhibition , pp. 76 - 85Publisher: Cambridge University PressPrint publication year: 2022