Large margin maximum entropy machines for classifier combination

Majority voting in classifier combination treats all base classifiers equally without considering their performance differences. By analyzing the constraints imposed by the margins of an ensemble classifier, a set of weights can be computed to give better prediction than the majority voting. We propose a regularized classifier combination strategy that maximize the entropy of probability weights assigned to base classifiers subjected to the margin constraints of the ensemble classifier. Furthermore, we show that a sparse solution with a set of support vectors for ensemble classifier can be obtained.