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SERBoost: Boosting with Expectation Regularization

A. Saffari, H. Grabner and H. Bischof
European Conference on Computer Vision (ECCV'08)
2008

Abstract

The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems. The complexity is mainly dominated by the base learners. The algorithm provides a margin regularizer for the boosting cost function and shows a principled way of utilizing prior knowledge. We demonstrate the performance of SERBoost on the Pascal VOC2006 set and compare it to other supervised and semi-supervised methods, where SERBoost shows improvements both in terms of classification accuracy and computational speed.


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@InProceedings{eth_biwi_00585,
  author = {A. Saffari and H. Grabner and H. Bischof},
  title = {SERBoost: Boosting with Expectation Regularization},
  booktitle = {European Conference on Computer Vision (ECCV'08)},
  year = {2008},
  series = {LNCS},
  publisher = {Springer},
  keywords = {}
}