Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
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.
Cite
Text
Seeger. "Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods." Neural Information Processing Systems, 2006.Markdown
[Seeger. "Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/seeger2006neurips-crossvalidation/)BibTeX
@inproceedings{seeger2006neurips-crossvalidation,
title = {{Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods}},
author = {Seeger, Matthias},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {1233-1240},
url = {https://mlanthology.org/neurips/2006/seeger2006neurips-crossvalidation/}
}