Active Learning with Hinted Support Vector Machine

4th Asian Conference on Machine Learning

Chun-Liang Li [e-mail] ; Hsuan-Tien Lin [e-mail]

PID  :  120744

来源: CEUR

【 摘 要 】

The abundance of realworld data and limited labeling budget calls for active learning, which is an important learning paradigm for reducing human labeling efforts. Many re cently developed active learning algorithms consider both uncertainty and representative ness when making querying decisions. However, exploiting representativeness with uncer tainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this paper, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by stateoftheart algorithms.