Joint Kernel Support Estimation for Structured Prediction
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We present a new technique for structured prediction that works in a hybrid generative/ discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random elds or structured out- put SVMs, the proposed method has the advantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works eciently and robustly in situations for which discriminative techniques have computational or statistical problems.
| Author(s): | Lampert, CH. and Blaschko, M. |
| Links: | |
| Journal: | Proceedings of the NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008) |
| Pages: | 1-4 |
| Year: | 2008 |
| Month: | December |
| Day: | 0 |
| BibTeX Type: | Conference Paper (inproceedings) |
| Event Name: | NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008) |
| Event Place: | Whistler, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max-Planck Institute for Biological Cybernetics, Tübingen, Germany |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{5610,
title = {Joint Kernel Support Estimation for Structured Prediction},
journal = {Proceedings of the NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008)},
abstract = {We present a new technique for structured
prediction that works in a hybrid generative/
discriminative way, using a one-class
support vector machine to model the joint
probability of (input, output)-pairs in a joint
reproducing kernel Hilbert space.
Compared to discriminative techniques, like
conditional random elds or structured out-
put SVMs, the proposed method has the advantage
that its training time depends only
on the number of training examples, not on
the size of the label space. Due to its generative
aspect, it is also very tolerant against
ambiguous, incomplete or incorrect labels.
Experiments on realistic data show that our
method works eciently and robustly in situations
for which discriminative techniques
have computational or statistical problems.},
pages = {1-4},
organization = {Max-Planck-Gesellschaft},
institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
school = {Biologische Kybernetik},
month = dec,
year = {2008},
author = {Lampert, CH. and Blaschko, M.},
month_numeric = {12}
}