Difference between revisions of "Chun-Nam John Yu, Hofmann , Learning structural SVMs with latent variables 2009"

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The paper argues that this is the first time latent variable are being used in large margin classifiers.Experiments were then performed in various domains  
 
The paper argues that this is the first time latent variable are being used in large margin classifiers.Experiments were then performed in various domains  
 
of computational Biology, IR and NLP to prove the generality of the proposed method.
 
of computational Biology, IR and NLP to prove the generality of the proposed method.
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== Experimentation ==
 
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The experimentations were performbed in many domains and results were as follows
 
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'''Discriminative Motif finding'''
 
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'''Error rate : '''
 
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Gibbs sampler        32.49%
 
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Latent Structural SVM  12%
 
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''' Noun Phrase Coreference via Clustering '''
 
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'''MITRE Loss:''' SVM cluster 41.3 Latent SVM 35.6
 
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The Structural SVM with latent variable also perform well in the task of Document Retrieval and outperformed List NET and Ranking SVM.
 

Revision as of 02:37, 1 October 2011

Summary

In this paper author talks about the use of latent variable in the structural SVM. The paper also identifies the formulation for which their exists effecient algorithm to find the local optimum using convex-concave optimization techniques. The paper argues that this is the first time latent variable are being used in large margin classifiers.Experiments were then performed in various domains of computational Biology, IR and NLP to prove the generality of the proposed method.

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