Budget semi-supervised learning

Zhou Zhi-Hua; Ng Michael; She Qiao-Qiao; Jiang Yuan

doi:10.1007/978-3-642-01307-2_57

Budget semi-supervised learning

Zhou Zhi-Hua^*, Ng Michael, She Qiao-Qiao, Jiang Yuan

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	588-595
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-01307-2_57
Publication status	Published - 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: 27 Apr 2009 → 30 Apr 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	27/04/09 → 30/04/09

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10.1007/978-3-642-01307-2_57

Cite this

Zhi-Hua, Z., Michael, N., Qiao-Qiao, S., & Yuan, J. (2009). Budget semi-supervised learning. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 588-595). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_57

@inproceedings{4f5ba850ecd4436880bfdc1868a13039,

title = "Budget semi-supervised learning",

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.",

author = "Zhou Zhi-Hua and Ng Michael and She Qiao-Qiao and Jiang Yuan",

note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 ; Conference date: 27-04-2009 Through 30-04-2009",

year = "2009",

doi = "10.1007/978-3-642-01307-2_57",

language = "English",

isbn = "3642013066",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "588--595",

booktitle = "13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009",

}

Zhi-Hua, Z, Michael, N, Qiao-Qiao, S & Yuan, J 2009, Budget semi-supervised learning. in 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5476 LNAI, pp. 588-595, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 27/04/09. https://doi.org/10.1007/978-3-642-01307-2_57

Budget semi-supervised learning. / Zhi-Hua, Zhou; Michael, Ng; Qiao-Qiao, She et al.
13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 588-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Michael, Ng

AU - Qiao-Qiao, She

AU - Yuan, Jiang

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N2 - 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.

AB - 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.

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Y2 - 27 April 2009 through 30 April 2009

ER -

Budget semi-supervised learning

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