Chun-Nam John Yu, Hofmann , Learning structural SVMs with latent variables 2009

From Cohen Courses
Jump to navigationJump to search

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.

Retrieved from "http://curtis.ml.cmu.edu/w/courses/index.php?title=Chun-Nam_John_Yu,_Hofmann_,_Learning_structural_SVMs_with_latent_variables_2009&oldid=8645"