Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)
Ofer Dekel, Yoram Singer
The standard Support Vector Machine formulation does not provide its user with the ability to explicitly control the number of support vectors used to deﬁne the generated classiﬁer. We present a modiﬁed version of SVM that allows the user to set a budget parameter B and focuses on minimizing the loss attained by the B worst-classiﬁed examples while ignoring the remaining examples. This idea can be used to derive sparse versions of both L1-SVM and L2-SVM. Technically, we obtain these new SVM variants by replacing the 1-norm in the standard SVM for- mulation with various interpolation-norms. We also adapt the SMO optimization algorithm to our setting and report on some preliminary experimental results.