@techreport{Ruping2009Kernelized,
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.},
address = {Dortmund},
author = {Stefan R\"{u}ping and Claus Weihs},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {310},
language = {eng},
number = {2009,02},
publisher = {Univ., SFB 475},
title = {Kernelized design of experiments},
type = {Technical Report // Sonderforschungsbereich 475, Komplexit\"{a}tsreduktion in Multivariaten Datenstrukturen, Universit\"{a}t Dortmund},
url = {http://hdl.handle.net/10419/36602},
year = {2009}
}
