Learning From Very-Few Labeled Examples with Soft Labels

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dc.contributor.author Mu, Y
dc.contributor.author Xu, M
dc.contributor.author Yan, S
dc.contributor.editor Technical Committee
dc.date.accessioned 2012-02-02T11:08:06Z
dc.date.issued 2010-01
dc.identifier.citation 2010 IEEE International Conference on Image Processing ICIP 2010 - Proceedings, 2010, pp. 3869 - 3872
dc.identifier.isbn 978-1-4244-7993-1
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16273
dc.description.abstract In this paper we propose Softboost, a novel Boosting al-gorithm which combines the merits of transductive and inductive learning approaches to attack the problem of learning from very few labeled training examples. In the transductive stage, soft labels of both the labeled and unlabeled samples are estimated based on a Markovian propagating procedure. While in the subsequent inductive stage, to efficiently handle out-of-sample data, we learn a weighted combination of simple rules in Boosting style, each of which maximizes confidence-weighted inter-class Kullback-Leibler (KL) divergence under current data distribution. Finally, experiments on toy dataset and USPS handwritten digits are presented to demonstrate its effectiveness.
dc.publisher IEEE Computer Society
dc.title Learning From Very-Few Labeled Examples with Soft Labels
dc.type Conference Proceeding
dc.parent 2010 IEEE International Conference on Image Processing ICIP 2010 - Proceedings
dc.journal.number en_US
dc.publocation Hongkong en_US
dc.identifier.startpage 3869 en_US
dc.identifier.endpage 3872 en_US
dc.cauo.name FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Image Processing
dc.for 080106 Image Processing
dc.personcode 109684
dc.percentage 100 en_US
dc.classification.name Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Conference on Image Processing en_US
dc.date.activity 20100926 en_US
dc.date.activity 2010-09-26
dc.location.activity Hongkong en_US
dc.description.keywords Boosting, soft label en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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