Active Learning with Hinted Support Vector Machine
Chunliang Li, Chun-Sung Ferng, Hsuan-Tien Lin · 2012
The abundance of real-world 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 state-of-the-art algorithms.