Maximal Margin Labeling for Multi-Topic Text Categorization
Abstract
In this paper, we address the problem of statistical learning for multi- topic text categorization (MTC), whose goal is to choose all relevant top- ics (a label) from a given set of topics. The proposed algorithm, Max- imal Margin Labeling (MML), treats all possible labels as independent classes and learns a multi-class classifier on the induced multi-class cate- gorization problem. To cope with the data sparseness caused by the huge number of possible labels, MML combines some prior knowledge about label prototypes and a maximal margin criterion in a novel way. Experi- ments with multi-topic Web pages show that MML outperforms existing learning algorithms including Support Vector Machines.
Cite
Text
Kazawa et al. "Maximal Margin Labeling for Multi-Topic Text Categorization." Neural Information Processing Systems, 2004.Markdown
[Kazawa et al. "Maximal Margin Labeling for Multi-Topic Text Categorization." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/kazawa2004neurips-maximal/)BibTeX
@inproceedings{kazawa2004neurips-maximal,
title = {{Maximal Margin Labeling for Multi-Topic Text Categorization}},
author = {Kazawa, Hideto and Izumitani, Tomonori and Taira, Hirotoshi and Maeda, Eisaku},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {649-656},
url = {https://mlanthology.org/neurips/2004/kazawa2004neurips-maximal/}
}