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From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy

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2013

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Springer

Zusammenfassung

Support 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.

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Gieseke, Fabian (2013): From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. KI - Künstliche Intelligenz: Vol. 27, No. 3. Springer. PISSN: 1610-1987. pp. 281-285

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