Gieseke, Fabian2018-01-082018-01-0820132013https://dl.gi.de/handle/20.500.12116/11364Support vector machines are among the most popular techniques in machine learning. Given sufficient labeled data, they often yield excellent results. However, for a variety of real-world tasks, the acquisition of sufficient labeled data can be very time-consuming; unlabeled data, on the other hand, can often be obtained easily in huge quantities. Semi-supervised support vector machines try to take advantage of these additional unlabeled patterns and have been successfully applied in this context. However, they induce a hard combinatorial optimization problem. In this work, we present two optimization strategies that address this task and evaluate the potential of the resulting implementations on real-world data sets, including an example from the field of astronomy.AstronomyMachine learningSemi-supervised learningSupport vector machinesText/Journal Article1610-1987