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ICML
2007
IEEE

Multiple instance learning for sparse positive bags

9 years 12 months ago
Multiple instance learning for sparse positive bags
We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other SVM-based MIL methods, our approach more directly enforces the desired constraint that at least one of the instances in a positive bag is positive. Using both artificial and real-world data, we experimentally demonstrate that our approach achieves greater accuracy than state-of-the-art MIL methods when positive bags are sparse, and performs competitively when they are not. In particular, our approach is the best performing method for image region classification.
Razvan C. Bunescu, Raymond J. Mooney
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Razvan C. Bunescu, Raymond J. Mooney
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