Learning with local and global consistency.

DMCA

Learning with local and global consistency. (2003)

Cached

Download Links

by Dengyong Zhou , Olivier Bousquet , Thomas Navin Lal , Jason Weston , Bernhard Schölkopf

Venue:	In NIPS,
Citations:	670 - 21 self

BibTeX

@INPROCEEDINGS{Zhou03learningwith,
    author = {Dengyong Zhou and Olivier Bousquet and Thomas Navin Lal and Jason Weston and Bernhard Schölkopf},
    title = {Learning with local and global consistency.},
    booktitle = {In NIPS,},
    year = {2003}
}

OpenURL

Abstract

Abstract 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 classification problems and demonstrates effective use of unlabeled data.

DMCA