UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Correcting sample selection bias by unlabeled data

Huang, J; Smola, AJ; Gretton, A; Borgwardt, KM; Schölkopf, B; (2007) Correcting sample selection bias by unlabeled data. In: UNSPECIFIED (pp. 601-608).

Full text not available from this repository.

Abstract

We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

Type: Book chapter
Title: Correcting sample selection bias by unlabeled data
ISBN-13: 9780262195683
URI: http://discovery.ucl.ac.uk/id/eprint/1334332
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item