{\cal U}Boost: Boosting with the Universum.

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 UBoost. \cal UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, \cal UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that \cal UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone. [ABSTRACT FROM AUTHOR]