From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy
| dc.contributor.author | Gieseke, Fabian | |
| dc.date.accessioned | 2018-01-08T09:16:41Z | |
| dc.date.available | 2018-01-08T09:16:41Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | 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. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11364 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 27, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Astronomy | |
| dc.subject | Machine learning | |
| dc.subject | Semi-supervised learning | |
| dc.subject | Support vector machines | |
| dc.title | From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 285 | |
| gi.citation.startPage | 281 |