A Support Vector Method for multivariate performance measures was recently introduced by Joachims (2005) . The underlying optimization problem is currently solved using cutting plane methods such as SVM-Perf and BMRM. One can show that these algorithms converge to an accurate solution in O 1 λ iterations, where λ is the trade-off parameter between the regularizer and the loss function. We present a smoothing strategy for multivariate performance scores, in particular precision/recall break-even point and ROCArea. When combined with Nesterov's accelerated gradient algorithm our smoothing strategy yields an optimization algorithm which converges to an accurate solution in iterations. Furthermore, the cost per iteration of our scheme is the same as that of SVM-Perf and BMRM. Empirical evaluation on a number of publicly available datasets shows that our method converges significantly faster than cutting plane methods without sacrificing generalization ability.
• Zhang et al. REF proposed a smoothing strategy for multivariate performance score optimization., in particular PREBP and AUC.
Smoothing Multivariate Performance Measures
ArXiv
ArXiv
Mathematics Computer Science