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.

• Type: Conference
• Year: 2009
• Where: PAKDD
• Authors: Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jiang
• Keywords: Data Mining, PAKDD 2009, Resource Budget, Semi-supervised Learning, Unlabeled Data