Empirical Inference
Talk
2006
Semi-Supervised Support Vector Machines and Application to Spam Filtering
PDF Web
Empirical Inference
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly.
| Author(s): | Zien, A. |
| Links: | |
| Year: | 2006 |
| Month: | September |
| Day: | 22 |
| BibTeX Type: | Talk (talk) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Name: | ECML Discovery Challenge Workshop |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@talk{4162,
title = {Semi-Supervised Support Vector Machines and Application to Spam Filtering},
abstract = {After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly.},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = sep,
year = {2006},
author = {Zien, A.},
month_numeric = {9}
}