Determination of hyper-parameters for kernel based classification and regression
| dc.contributor.author | Christmann, Andreas | |
| dc.contributor.author | Luebke, Karsten | |
| dc.contributor.author | Marin-Galiano, Marcos | |
| dc.contributor.author | RĂ¼ping, Stefan | |
| dc.date.accessioned | 2005-11-07T11:39:49Z | |
| dc.date.available | 2005-11-07T11:39:49Z | |
| dc.date.issued | 2005-11-07T11:39:49Z | |
| dc.description.abstract | We investigate methods to determine appropriate choices of the hyper-parameters for kernel based methods. Support vector classification, kernel logistic regression and support vector regression are considered. Grid search, Nelder-Mead algorithm and pattern search algorithm are used. | en |
| dc.format.extent | 502994 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2003/21667 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-14494 | |
| dc.language.iso | en | |
| dc.subject | convex risk minimization | en |
| dc.subject | kernel logistic regression | en |
| dc.subject | statistical machine learning | en |
| dc.subject | support vector machine | en |
| dc.subject | support vector regression | en |
| dc.subject.ddc | 004 | |
| dc.title | Determination of hyper-parameters for kernel based classification and regression | en |
| dc.type | Text | |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |