Rüping, StefanWeihs, Claus2009-04-302009-04-302009-04-30http://hdl.handle.net/2003/26094http://dx.doi.org/10.17877/DE290R-8240This 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.en004Text