title: UBoost: boosting with the Universum.
abstract: 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 UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.
authors.affiliation: Australian Center for Visual Technologies, and School of Computer Science, University of Adelaide, North Terrace, South Australia 5005, Australia. chunhua.shen@adelaide.edu.au
authors.forename: Chunhua
authors.initials: C
authors.lastname: Shen
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Fumin
authors.initials: F
authors.lastname: Shen
authors.forename: Hanzi
authors.initials: H
authors.lastname: Wang