Support Vector Regression

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.