Normalized Hierarchical SVM
Machine Learning
2016-03-07 v2
Abstract
We present improved methods of using structured SVMs in a large-scale hierarchical classification problem, that is when labels are leaves, or sets of leaves, in a tree or a DAG. We examine the need to normalize both the regularization and the margin and show how doing so significantly improves performance, including allowing achieving state-of-the-art results where unnormalized structured SVMs do not perform better than flat models. We also describe a further extension of hierarchical SVMs that highlight the connection between hierarchical SVMs and matrix factorization models.
Cite
@article{arxiv.1508.02479,
title = {Normalized Hierarchical SVM},
author = {Heejin Choi and Yutaka Sasaki and Nathan Srebro},
journal= {arXiv preprint arXiv:1508.02479},
year = {2016}
}