On Regularization Parameter Estimation under Covariate Shift

Wouter Kouw, Marco Loog

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting. In such a setting, there are differences between the distributions generating the training data (source domain) and the test data (target domain). The usual cross-validation
procedure requires validation data, which can not be obtained from the unlabeled target data. The problem is that if one decides to use source validation data, the regularization parameter is underestimated. One possible solution is to scale the source validation data through importance weighting, but we show that
this correction is not sufficient. We conclude the paper with an empirical analysis of the effect of several importance weight estimators on the estimation of the regularization parameter.
Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)978-1-5090-4847-2
ISBN (Print)978-1-5090-4848-9
Publication statusPublished - 2016
EventICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23


ConferenceICPR 2016


  • Training
  • Pattern recognition
  • Estimation
  • Risk management
  • Parameter estimation
  • Training data
  • Temperature measurement

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