Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/36602
Authors: 
Rüping, Stefan
Weihs, Claus
Year of Publication: 
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

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