Empirical Inference
Conference Paper
2007
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
PDF Web
Empirical Inference
We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.
| Author(s): | Seeger, M. |
| Links: | |
| Book Title: | Advances in Neural Information Processing Systems 19 |
| Journal: | Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference |
| Pages: | 1233-1240 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Editors: | Sch{\"o}lkopf, B. , J. Platt, T. Hofmann |
| Publisher: | MIT Press |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-262-19568-2 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{4168,
title = {Cross-Validation Optimization for Large Scale Hierarchical
Classification Kernel Methods},
journal = {Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference},
booktitle = {Advances in Neural Information Processing Systems 19},
abstract = {We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and
predictive probabilities are estimated. We demonstrate our
approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.},
pages = {1233-1240},
editors = {Sch{\"o}lkopf, B. , J. Platt, T. Hofmann},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = sep,
year = {2007},
author = {Seeger, M.},
month_numeric = {9}
}