%%% -*-BibTeX-*-
@article{UBoost2011Shen,
    author = {Chunhua Shen and Peng Wang and Fumin Shen and Hanzi Wang},
    title  = {{UBoost}: {B}oosting with the {U}niversum},
    journal= {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    volume = {34},
    number = {4},
    year   = {2012},
    month  = {April},
    pages  = {825--832},
    eprint = {},
    pdf    = {http://hdl.handle.net/2440/67027},
    venue  = {TPAMI},
    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. 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.
    },
}