A simple and effective lagrangian-based combinatorial algorithm for S3VMs

Many optimization techniques have been developed in the last decade to include the unlabeled patterns in the Support Vector Machines formulation. Two broad strategies are followed: continuous and combinatorial. The approach presented in this paper belongs to the latter family and is especially suitable when a fair estimation of the proportion of positive and negative samples is available. Our method is very simple and requires a very light parameter selection. Experiments on both artificial and real-world datasets have been carried out, proving the effectiveness and the efficiency of the proposed algorithm.