Structured Prediction with Relative Margin

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Structured Prediction with Relative Margin
In structured prediction problems, outputs are not confined to binary labels; they are often complex objects such as sequences, trees, or alignments. Support Vector Machine (SVM) methods have been successfully extended to such prediction problems. However, recent developments in large margin methods show that higher order information can be exploited for even better generalization. This article first points out a shortcoming of the SVM approach for the structured prediction; an efficient formulation is then presented to overcome the problem. The proposed algorithm exploits the fact that both the minimum and the maximum of quantities of interest are often efficiently computable even though quantities such as mean, median and variance may not be. The resulting formulation produces state-of-the-art performance on sequence learning problems. Dramatic improvements are also seen on multi-class problems.
Pannagadatta K. Shivaswamy, Tony Jebara
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Authors Pannagadatta K. Shivaswamy, Tony Jebara
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