A support vector method for multivariate performance measures

This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Thorsten Joachims
• Keywords: ICML 2005, Machine Learning, Multivariate Nonlinear Performance, Multivariate Prediction Approach, Non-linear Performance Measures