Kernel-Based Learning of Hierarchical Multilabel Classification Models
Abstract
We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set.
Cite
Text
Rousu et al. "Kernel-Based Learning of Hierarchical Multilabel Classification Models." Journal of Machine Learning Research, 2006.Markdown
[Rousu et al. "Kernel-Based Learning of Hierarchical Multilabel Classification Models." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/rousu2006jmlr-kernelbased/)BibTeX
@article{rousu2006jmlr-kernelbased,
title = {{Kernel-Based Learning of Hierarchical Multilabel Classification Models}},
author = {Rousu, Juho and Saunders, Craig and Szedmak, Sandor and Shawe-Taylor, John},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {1601-1626},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/rousu2006jmlr-kernelbased/}
}