A Support Vector Classification Model with Partial Empirical Risks Given

Abstract

A novel model of support vector classification with partial empirical risks given (P-SVC) is proposed. A sequential minimal optimization for P-SVC is also provided. P-SVC is an extension of the classical support vector classification (C-SVC) and can be used in the case where partial empirical risks are requested. The experiments on some artificial and benchmark datasets show P-SVC obtains a better classification accuracy and a more stable classification result than C-SVC does when partial empirical risks are known.