Empirical Inference
Empirical Inference
Empirical Inference
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.
| Author(s): | Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Schölkopf, B. |
| Links: | |
| Book Title: | Advances in Neural Information Processing Systems 16 |
| Journal: | Advances in Neural Information Processing Systems |
| Pages: | 321-328 |
| Year: | 2004 |
| Month: | June |
| Day: | 0 |
| Editors: | S Thrun and LK Saul and B Sch{\"o}lkopf |
| Publisher: | MIT Press |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | 17th Annual Conference on Neural Information Processing Systems (NIPS 2003) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics |
| ISBN: | 0-262-20152-6 |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{2333,
title = {Learning with Local and Global Consistency},
journal = {Advances in Neural Information Processing Systems},
booktitle = {Advances in Neural Information Processing Systems 16},
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.},
pages = {321-328},
editors = {S Thrun and LK Saul and B Sch{\"o}lkopf},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = jun,
year = {2004},
author = {Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Sch{\"o}lkopf, B.},
month_numeric = {6}
}