Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, Bernhard Schölkopf
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive in- ference. A principled approach to semi-supervised learning is to design a classifying function which is suf(cid:2)ciently 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 clas- si(cid:2)cation problems and demonstrates effective use of unlabeled data.