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Conference Paper: Budget semi-supervised learning
Title | Budget semi-supervised learning |
---|---|
Authors | |
Issue Date | 2009 |
Publisher | Springer. |
Citation | 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 27-30 April 2009. In Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009: Proceedings, 2009, p. 588-595 How to Cite? |
Abstract | In this paper we propose to study budget semi-supervised learning,i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-supervised learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly,the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method. © Springer-Verlag Berlin Heidelberg 2009. |
Persistent Identifier | http://hdl.handle.net/10722/276841 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science ; 5476 |
DC Field | Value | Language |
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dc.contributor.author | Zhi-Hua, Zhou | - |
dc.contributor.author | Michael, Ng | - |
dc.contributor.author | Qiao-Qiao, She | - |
dc.contributor.author | Yuan, Jiang | - |
dc.date.accessioned | 2019-09-18T08:34:49Z | - |
dc.date.available | 2019-09-18T08:34:49Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 27-30 April 2009. In Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009: Proceedings, 2009, p. 588-595 | - |
dc.identifier.isbn | 9783642013065 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276841 | - |
dc.description.abstract | In this paper we propose to study budget semi-supervised learning,i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-supervised learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly,the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method. © Springer-Verlag Berlin Heidelberg 2009. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009: Proceedings | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 5476 | - |
dc.title | Budget semi-supervised learning | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-01307-2_57 | - |
dc.identifier.scopus | eid_2-s2.0-67650656582 | - |
dc.identifier.spage | 588 | - |
dc.identifier.epage | 595 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Berlin | - |
dc.identifier.issnl | 0302-9743 | - |