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Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen, Technische Universität Dortmund >
Technical Reports, SFB 475, TU Dortmund >
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http://hdl.handle.net/10419/36602
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| Title: | | Kernelized design of experiments  |
| Authors: | | Rüping, Stefan Weihs, Claus |
| Issue Date: | | 2009 |
| Series/Report no.: | | Technical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2009,02 |
| 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. |
| Document Type: | | Working Paper |
| Appears in Collections: | | Technical Reports, SFB 475, TU Dortmund
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