Adapting SVM classifiers to data with shifted distributions

Abstract

Many data mining applications can benefit from adapting existing classifiers to new data with shifted distributions. In this paper, we present Adaptive Support Vector Machine (Adapt-SVM) as an efficient model for adapting a SVM classifier trained from one dataset to a new dataset where only limited labeled examples are available. By introducing a new regularizer into SVM's objective function, Adapt-SVM aims to minimize both the classification error over the training examples, and the discrepancy between the adapted and original classifier. We also propose a selective sampling strategy based on the loss minimization principle to seed the most informative examples for classifier adaptation. Experiments on an artificial classification task and on a benchmark video classification task shows that Adapt-SVM outperforms several baseline methods in terms of accuracy and/or efficiency. © 2007 IEEE.