Support Vector Regression
Darius M. Dziuda
Abstract
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.
Topics & Concepts
Support vector machineGeneralizationQuadratic programmingStructural risk minimizationComputer scienceFunction (biology)ComputationQuadratic equationSeries (stratigraphy)RegressionMathematical optimizationFeature vectorMathematicsArtificial intelligenceAlgorithmStatisticsGeometryEvolutionary biologyPaleontologyMathematical analysisBiologyNeural Networks and ApplicationsGaussian Processes and Bayesian InferenceFace and Expression Recognition