title: Active Learning Using Hint Information.
abstract: The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, 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. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.
authors.affiliation: Department of Computer Science, National Taiwan University, Taipei, 106, Taiwan b97018@csie.ntu.edu.tw.
authors.forename: Chun-Liang
authors.initials: CL
authors.lastname: Li
authors.affiliation: Department of Computer Science, National Taiwan University, Taipei, 106, Taiwan r99922054@csie.ntu.edu.tw.
authors.forename: Chun-Sung
authors.initials: CS
authors.lastname: Ferng
authors.affiliation: Department of Computer Science, National Taiwan University, Taipei, 106, Taiwan htlin@csie.ntu.edu.tw.
authors.forename: Hsuan-Tien
authors.initials: HT
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*