NDLI: Structured Prediction with Relative Margin

Content Provider	IEEE Xplore Digital Library
Author	Shivaswamy, P. Jebara, T.
Copyright Year	2009
Abstract	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.
Starting Page	281
Ending Page	287
File Size	280956
Page Count	7
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Virtual colonoscopy Boosting Application software Markov random fields Support vector machines Computer science Support Vectors Hidden Markov models Machine learning Natural language processing Large Relative Margin Structured Prediction Kernel
Content Type	Text
Resource Type	Article

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