Regularized semi-supervised classification on manifold

Zhao, L; Luo, S; Zhao, Y; Liao, L; Wang, Z

Regularized semi-supervised classification on manifold

Zhao, L Luo, S Zhao, Y Liao, L Wang, Z

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Publisher:: Springer-Verlag Berlin
Publication Type:: Conference Proceeding
Citation:: Advances In Knowledge Discovery And Data Mining, Proceedings of the 12th Pacific-Asia Conference, 2006, pp. 20 - 29
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Zhao, L	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Liao, L	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.editor	Ng, WK	en_US
dc.contributor.editor	Kitsuregawa, M	en_US
dc.contributor.editor	Li, J	en_US
dc.contributor.editor	Chang, K	en_US
dc.date	2006-04-09	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Advances In Knowledge Discovery And Data Mining, Proceedings of the 12th Pacific-Asia Conference, 2006, pp. 20 - 29	en_US
dc.identifier.isbn	3-540-33206-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2349
dc.description.abstract	Semi-supervised learning gets estimated marginal distribution P-X with a large number of unlabeled examples and then constrains the conditional probability p(y vertical bar x) with a few labeled examples. In this paper, we focus on a regularization appro	en_US
dc.publisher	Springer-Verlag Berlin	en_US
dc.relation.ispartof	Advances In Knowledge Discovery And Data Mining, Proceedings of the 12th Pacific-Asia Conference	en_US
dc.relation.ispartof	Pacific-Asia Conference on Knowledge Discovery and Data Mining	en_US
dc.relation.isbasedon	https://doi.org/10.1007/11731139_5	en_us
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Advances In Knowledge Discovery And Data Mining, Proceedings of the 12th Pacific-Asia Conference. The final authenticated version is available online at: https://doi.org/10.1007/11731139_5.	en_US
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Pacific-Asia Conference on Knowledge Discovery and Data Mining. The final authenticated version is available online at: https://doi.org/10.1007/11731139_5.	en_US
dc.title	Regularized semi-supervised classification on manifold	en_US
dc.type	Conference Proceeding
utslib.location	Berlin, Germany	en_US
utslib.location.activity	Singapore, Singapore	en_US
utslib.for	0804 Data Format	en_US
dc.location.activity	Singapore, Singapore	en_US
dc.location.activity	Nice, France
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Berlin, Germany	en_US
pubs.start-date	2006-04-09	en_US

Abstract:

Semi-supervised learning gets estimated marginal distribution P-X with a large number of unlabeled examples and then constrains the conditional probability p(y vertical bar x) with a few labeled examples. In this paper, we focus on a regularization appro

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http://hdl.handle.net/10453/2349