Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rüping, 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.identifier.uri | http://hdl.handle.net/2003/26094 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-8240 | - |
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.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 | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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tr02-09.pdf | DNB | 125.82 kB | Adobe PDF | View/Open |
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This item is protected by original copyright
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