A nonparallel support vector machine for a classification problem with universum learning
Journal of Computational and Applied Mathematics
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It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name {\cal U}Boost. {\cal U}Boost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, {\cal U}Boost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that {\cal U}Boost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.