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Support vector machine (SVM) training is an active research area since thedawn of the method. In recent years there has been increasing interest inspecialized solvers for the important case of linear models. The algorithmpresented by Hsieh et al., probably best known under the name of the"liblinear" implementation, marks a major breakthrough. The method is analog toestablished dual decomposition algorithms for training of non-linear SVMs, butwith greatly reduced computational complexity per update step. This comes atthe cost of not keeping track of the gradient of the objective any more, whichexcludes the application of highly developed working set selection algorithms.We present an algorithmic improvement to this method. We replace uniformworking set selection with an online adaptation of selection frequencies. Theadaptation criterion is inspired by modern second order working set selectionmethods. The same mechanism replaces the shrinking heuristic. This noveltechnique speeds up training in some cases by more than an order of magnitude.

Accelerated Linear SVM Training with Adaptive Variable Selection Frequencies