| dc.contributor.author | Widmer, Christian | |
| dc.contributor.author | Kloft, Marius | |
| dc.contributor.author | Lou, Xinghua | |
| dc.contributor.author | Rätsch, Gunnar | |
| dc.date | 2014-02-01 | |
| dc.date.accessioned | 2018-01-08T09:17:01Z | |
| dc.date.available | 2018-01-08T09:17:01Z | |
| dc.date.issued | 2014 | |
| dc.identifier.issn | 1610-1987 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/11390 | |
| dc.description.abstract | The aim of multitask learning is to improve the generalization performance of a set of related tasks by exploiting complementary information about the tasks. In this paper, we review established approaches for regularization based multitask learning, sketch some recent developments, and demonstrate their applications in Computational Biology and Biological Imaging. | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 28, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Multitask learning | |
| dc.subject | Regularized risk minimization | |
| dc.subject | Transfer learning | |
| dc.title | Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging | |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 29-33 | |
| gi.identifier.doi | 10.1007/s13218-013-0283-y | |
