Kernelized design of experiments
| dc.contributor.author | RĂ¼ping, Stefan | |
| dc.contributor.author | Weihs, Claus | |
| dc.date.accessioned | 2009-04-30T10:38:27Z | |
| dc.date.available | 2009-04-30T10:38:27Z | |
| dc.date.issued | 2009-04-30T10:38:27Z | |
| dc.description.abstract | This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented that selects a set of k instances to be chosen in order to maximize the prediction performance of a support vector machine. It is shown that the algorithm significantly outperforms related approaches on a number of real-world datasets. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/26094 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-8240 | |
| dc.language.iso | en | de |
| dc.subject.ddc | 004 | |
| dc.title | Kernelized design of experiments | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |