Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Fundamental tools and concepts
- Part II Tools for fully data-driven machine learning
- 5 Automatic feature design for regression
- 6 Automatic feature design for classification
- 7 Kernels, backpropagation, and regularized cross-validation
- Part III Methods for large scale machine learning
- Part IV Appendices
- References
- Index
This chapter is part of a book that is no longer available to purchase from Cambridge Core
7 - Kernels, backpropagation, and regularized cross-validation
from Part II - Tools for fully data-driven machine learning
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Fundamental tools and concepts
- Part II Tools for fully data-driven machine learning
- 5 Automatic feature design for regression
- 6 Automatic feature design for classification
- 7 Kernels, backpropagation, and regularized cross-validation
- Part III Methods for large scale machine learning
- Part IV Appendices
- References
- Index
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- Information
- Machine Learning RefinedFoundations, Algorithms, and Applications, pp. 195 - 216Publisher: Cambridge University PressPrint publication year: 2016