Learning from General Label Constraints

@inproceedings{Bie2004LearningFG,
  title={Learning from General Label Constraints},
  author={Tijl De Bie and Johan A. K. Suykens and Bart De Moor},
  booktitle={SSPR/SPR},
  year={2004}
}
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and in vision however show that this setting often is an oversimplification of reality. While label information is of course invaluable in most cases, it would be a huge waste to ignore the information on the cluster structure that is present in an (often much larger) unlabeled sample set. Several recent contributions deal… CONTINUE READING
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