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
- 7 Kernel-based regression and regularization analysis
- 8 Linear regression and discriminant analysis for supervised classification
- 9 Kernel ridge regression for super vised classification
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- Part VII Kernel methods and statistical estimation theory
- Part VIII Appendices
- References
- Index
8 - Linear regression and discriminant analysis for supervised classification
from Part IV - Kernel ridge regressors and variants
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
- 7 Kernel-based regression and regularization analysis
- 8 Linear regression and discriminant analysis for supervised classification
- 9 Kernel ridge regression for super vised classification
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- Part VII Kernel methods and statistical estimation theory
- Part VIII Appendices
- References
- Index
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- Kernel Methods and Machine Learning , pp. 248 - 281Publisher: Cambridge University PressPrint publication year: 2014
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