Authors: | Rüping, Stefan Weihs, Claus |
Title: | Kernelized design of experiments |
Language (ISO): | en |
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. |
URI: | http://hdl.handle.net/2003/26094 http://dx.doi.org/10.17877/DE290R-8240 |
Issue Date: | 2009-04-30T10:38:27Z |
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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