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PAKDD
2009
ACM

Budget Semi-supervised Learning

11 years 6 months ago
Budget Semi-supervised Learning
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.
Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jian
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where PAKDD
Authors Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jiang
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