Second-order online active learning and its applications

• Main Authors: HAO, Shuji, LU, Jing, ZHAO, Peilin, ZHANG, Chi, HOI, Steven C. H., MIAO, Chunyan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Algorithm design and analysis; Labeling; Active Learning; Online Learning; Prediction algorithms; Machine learning algorithms; Malicious websites detection; Predictive models; Training; Databases and Information Systems; Numerical Analysis and Scientific Computing; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/4132; https://ink.library.smu.edu.sg/context/sis_research/article/5135/viewcontent/Second_order_Online_Active_Learning_and_Its.pdf
• Institution: Singapore Management University

The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effectiv...