Recall Systems: Effcient Learning and Use of Category Indices
Omid Madani, Wiley Greiner, David Kempe, Mohammad R. Salavatipour;
JMLR W&P 2:307-314, 2007.
We introduce the framework of recall systems for efficient learning and retrieval of categories when the number of categories is large. A recallsystem 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.