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
- Part VIII Appendices
- References
- Index
Part VIII - Appendices
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
- Part VIII Appendices
- References
- Index
Summary
Appendix A reviews useful validation techniques and test schemes for learning models.
Appendix B covers supervised classifiers without an explicit learning process and the popular and fundamental Bayes classifiers.
- Type
- Chapter
- Information
- Kernel Methods and Machine Learning , pp. 537 - 538Publisher: Cambridge University PressPrint publication year: 2014