Recall Systems: Effcient Learning and Use of Category Indices
Abstract
We introduce the framework of recall systems for efficient learning and retrieval of categories when the number of categories is large. A recall system here is a simple feature-based intermediate filtering step which reduces the potential categories for an instance to a small manageable set. The correct categories from this set can then be determined using traditional classifiers. We present a formalization of the index learning problem and establish NP-hardness and approximation hardness. We proceed to give an efficient heuristic for learning indices, and evaluate it on several large data sets. In our experiments, the index is learned within minutes, and reduces the number of categories by several orders of magnitude, without affecting the quality of classification overall.
Cite
Text
Madani et al. "Recall Systems: Effcient Learning and Use of Category Indices." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Madani et al. "Recall Systems: Effcient Learning and Use of Category Indices." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/madani2007aistats-recall/)BibTeX
@inproceedings{madani2007aistats-recall,
title = {{Recall Systems: Effcient Learning and Use of Category Indices}},
author = {Madani, Omid and Greiner, Wiley and Kempe, David and Salavatipour, Mohammad R.},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {307-314},
volume = {2},
url = {https://mlanthology.org/aistats/2007/madani2007aistats-recall/}
}