On semi-supervised classification. Krishnapuram, B.; Williams, D.; Xue, Y.; Carin, L.; Figueiredo, M.; and Hartemink, A. J In Advances in neural information processing systems, pages 721--728, 2004.
abstract   bibtex   
A graph-based prior is proposed for parametric semi-supervised classi- fication. The prior utilizes both labelled and unlabelled data; it also in- tegrates features from multiple views of a given sample ( e.g. , multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff be- tween the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is per- formed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors
@InProceedings{Krishnapuram2004,
  Title                    = {On semi-supervised classification},
  Author                   = {Krishnapuram, Balaji and Williams, David and Xue, Ya and Carin, Lawrence and Figueiredo, M{\'a}rio and Hartemink, Alexander J},
  Booktitle                = {Advances in neural information processing systems},
  Year                     = {2004},
  Pages                    = {721--728},

  Abstract                 = {A graph-based prior is proposed for parametric semi-supervised classi-
fication. The prior utilizes both labelled and unlabelled data; it also in-
tegrates features from multiple views of a given sample (
e.g.
, multiple
sensors), thus implementing a Bayesian form of co-training. An EM
algorithm for training the classifier automatically adjusts the tradeoff be-
tween the contributions of: (a) the labelled data; (b) the unlabelled data;
and (c) the co-training information. Active label query selection is per-
formed using a mutual information based criterion that explicitly uses the
unlabelled data and the co-training information. Encouraging results are
presented on public benchmarks and on measured data from single and
multiple sensors},
  Review                   = {Has a lot of math, but seems to rely on distance based clustering and EM learning.},
  Timestamp                = {2015.05.12}
}
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