Multivariate Biomarker Discovery Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
Book contents
- Multivariate Biomarker Discovery
- Multivariate Biomarker Discovery
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Part I Framework for Multivariate Biomarker Discovery
- Part II Regression Methods for Estimation
- 6 Basic Regression Methods
- 7 Regularized Regression Methods
- 8 Regression with Random Forests
- 9 Support Vector Regression
- Part III Classification Methods
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
9 - Support Vector Regression
from Part II - Regression Methods for Estimation
Published online by Cambridge University Press: 30 May 2024
Book contents
- Multivariate Biomarker Discovery
- Multivariate Biomarker Discovery
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Part I Framework for Multivariate Biomarker Discovery
- Part II Regression Methods for Estimation
- 6 Basic Regression Methods
- 7 Regularized Regression Methods
- 8 Regression with Random Forests
- 9 Support Vector Regression
- Part III Classification Methods
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
Summary
Chapter 9 presents support vector regression (SVR), a relatively newer supervised learning algorithm for predictive regression modeling, which – like random forests for regression – also may outperform the least-squares-based methods. Discussed is ε-insensitive loss used by SVR, the ε-tube concept, as well as algorithms for linear and nonlinear SVRs.
Keywords
- Type
- Chapter
- Information
- Multivariate Biomarker DiscoveryData Science Methods for Efficient Analysis of High-Dimensional Biomedical Data, pp. 136 - 146Publisher: Cambridge University PressPrint publication year: 2024