On-the-fly domain adaptation of binary classifiers

This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover, the mixture weights and the priors are only available at runtime. We present a theoretical solution to this problem, and demonstrate the effectiveness of the proposed approach on a real computer vision application. Our theoretical solution is applicable to any classifier approximating Bayes' classifier.