Empirical Inference
Talk
2004
Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking
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. |
| Year: | 2004 |
| Month: | January |
| Day: | 0 |
| Bibtex Type: | Talk (talk) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Place: | The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences, Beijing, China |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@talk{2589,
title = {Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking},
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.},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jan,
year = {2004},
slug = {2589},
author = {Zhou, D.},
month_numeric = {1}
}