Difference between revisions of "Chun-Nam John Yu, Hofmann , Learning structural SVMs with latent variables 2009"
Line 1: | Line 1: | ||
− | |||
− | |||
− | |||
== Summary == | == Summary == | ||
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.
Experimentation
The experimentations were performbed in many domains and results were as follows
Discriminative Motif finding
Error rate : Gibbs sampler 32.49% Latent Structural SVM 12%
Noun Phrase Coreference via Clustering
MITRE Loss: SVM cluster 41.3 Latent SVM 35.6
The Structural SVM with latent variable also perform well in the task of Document Retrieval and outperformed List NET and Ranking SVM.