Corpus ID: 508435

Learning with Local and Global Consistency

@inproceedings{Zhou2003LearningWL,
  title={Learning with Local and Global Consistency},
  author={Dengyong Zhou and O. Bousquet and T. N. Lal and J. Weston and B. Sch{\"o}lkopf},
  booktitle={NIPS},
  year={2003}
}

Dengyong Zhou, O. Bousquet, +2 authors B. Schölkopf

Published in NIPS 2003

Computer Science, Mathematics

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of… CONTINUE READING

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