Learning from partially supervised data using mixture models and belief functions

@article{Cme2009LearningFP,
  title={Learning from partially supervised data using mixture models and belief functions},
  author={Etienne C{\^o}me and Latifa Oukhellou and Thierry Denoeux and Patrice Aknin},
  journal={Pattern Recognition},
  year={2009},
  volume={42},
  pages={334-348}
}
This paper addresses classification problems in which the class membership of training data is only partially known. Each learning sample is assumed to consist in a feature vector xi ∈ X and an imprecise and/or uncertain “soft” label mi defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are… CONTINUE READING
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