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 SizeFormat 
tr02-09.pdfDNB125.82 kBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.