Utilizing information theoretic diversity for SVM active learning

Charlie K. Dagli, Shyamsundar Rajaram, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Incrementally learning from a large number of unlabeled examples continues to be an active area of research in pattern recognition. Active Learning has made great strides in recent years to address this problem, taking advantage of SVMs to develop robust learning systems. Recently, diversity sampling for SVM active learning has garnered much attention. In this work we propose a fundamentally motivated view of diversity for SVM active learning based on an information-theoretic diversity measure. Comparative testing on a database from the small-sample learning problem of image retrieval is done and thoughts for future work are presented.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages506-511
Number of pages6
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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