Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Part I Machine learning and kernel vector spaces
- Part II Dimension-reduction: PCA/KPCA and feature selection
- Part III Unsupervised learning models for cluster analysis
- Part IV Kernel ridge regressors and variants
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- Part VII Kernel methods and statistical estimation theory
- 14 Statistical regression analysis and errors-in-variables models
- 15 Kernel methods for estimation, prediction, and system identification
- Part VIII Appendices
- References
- Index
15 - Kernel methods for estimation, prediction, and system identification
from Part VII - Kernel methods and statistical estimation theory
Published online by Cambridge University Press: 05 July 2014
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Part I Machine learning and kernel vector spaces
- Part II Dimension-reduction: PCA/KPCA and feature selection
- Part III Unsupervised learning models for cluster analysis
- Part IV Kernel ridge regressors and variants
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- Part VII Kernel methods and statistical estimation theory
- 14 Statistical regression analysis and errors-in-variables models
- 15 Kernel methods for estimation, prediction, and system identification
- Part VIII Appendices
- References
- Index
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- Information
- Kernel Methods and Machine Learning , pp. 494 - 536Publisher: Cambridge University PressPrint publication year: 2014