Name
Abstract | ||
|---|---|---|
We address in this paper the question of how the knowledge of the marginal distribution P(x) can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We, also propose practical implementations. |
Year | Venue | Keywords |
|---|---|---|
2003 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16 | semi supervised learning |
Field | DocType | Volume |
Mathematical optimization,Stability (learning theory),Semi-supervised learning,Instance-based learning,Computer science,Empirical risk minimization,Wake-sleep algorithm,Supervised learning,Unsupervised learning,Artificial intelligence,Machine learning,Learning classifier system | Conference | 16 |
ISSN | Citations | PageRank |
1049-5258 | 34 | 7.56 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Bousquet | 1 | 4593 | 359.65 |
| olivier chapelle | 2 | 5960 | 455.12 |
| Matthias Hein | 3 | 663 | 62.80 |