Diversity Regularized Machine

Ensemble methods, which train multiple learners for a task, are among the state-of-the-art learning approaches. The diversity of the component learners has been recognized as a key to a good ensemble, and existing ensemble methods try different ways to encourage diversity, mostly by heuristics. In this paper, we propose the diversity regularized machine (DRM) in a mathematical programming framework, which efficiently generates an ensemble of diverse support vector machines (SVMs). Theoretical analysis discloses that the diversity constraint used in DRM can lead to an effective reduction on its hypothesis space complexity, implying that the diversity control in ensemble methods indeed plays a role of regularization as in popular statistical learning approaches. Experiments show that DRM can significantly improve generalization ability and is superior to some state-of-the-art SVM ensemble methods.