AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

Font Size: 
Learning Optimal Subsets with Implicit User Preferences
Yunsong Guo, Carla Gomes

Last modified: 2009-06-26


We study the problem of learning an optimal subset from a larger ground set of items, where the optimality criterion is defined by an unknown preference function. We model the problem as a discriminative structural learning problem and solve it using a Structural Support Vector Machine (SSVM) that optimizes a set accuracy performance measure representing set similarities. Our approach departs from previous approaches since we do not explicitly learn a pre-defined preference function. Experimental results on both a synthetic problem domain and a real-world face image subset selection problem show that our method significantly outperforms previous learning approaches for such problems.


optimal subset;implicit preference;structured learning

Full Text: PDF