Exploiting Known Taxonomies in Learning Overlapping Concepts
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 708-712.
Abstract
Many real-world classification problems involve large numbers of overlapping categories that are arranged in a hierarchy or taxonomy. We propose to incorporate prior knowledge on category taxonomy directly into the learning architecture. We present two concrete multi-label classification methods, a generalized version of Perceptron and a hierarchical multi-label SVM learning. Our method works with arbitrary, not necessarily singly connected taxonomies, and can be applied more generally in settings where categories are characterized by attributes and relations that are not necessarily induced by a taxonomy. Experimental results on WIPO-alpha collection show that our hierarchical methods bring significant performance improvement.
@inproceedings{ijcai2007p113,
title = {Exploiting Known Taxonomies in Learning Overlapping Concepts},
author = {Cai, Lijuan and Hofmann, Thomas},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {708--712},
year = {2007},
url = {https://ijcai.org/proceedings/2007/113},
}
TY - CPAPER TI - Exploiting Known Taxonomies in Learning Overlapping Concepts AU - Cai, Lijuan AU - Hofmann, Thomas T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 708 EP - 712 UR - https://ijcai.org/proceedings/2007/113 ER -Copied
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