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SOAL: Second-order Online Active Learning


Authors: S. Hao, P. Zhao, J. Lu, S. C. H. Hoi, C. Miao, and C. Zhang
Title: SOAL: Second-order Online Active Learning
Abstract: This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and sub-optimal exploitation of available information when querying the labeled data. To overcome the limitations, in this paper, we present a new framework of Second-order Online Active Learning (SOAL), which fully exploits both first-order and second-order information to achieve high learning accuracy with low labeling cost. We conduct both theoretical analysis and empirical studies for evaluating the proposed SOAL algorithm extensively. The encouraging results show clear advantages of the proposed algorithm over a family of state-of-the-art online active learning algorithms.
Keywords: 
Conference Name: IEEE 16th International Conference on Data Mining (ICDM’16)
Location: Barcelona, Spain
Publisher: IEEE
Year: 2016
Accepted PDF File: SOAL_Second-order_Online_Active_Learning_accepted.pdf
Permanent Link: https://dx.doi.org/10.1109/ICDM.2016.0115
Reference: S. Hao, P. Zhao, J. Lu, S. C. H. Hoi, C. Miao, and C. Zhang, “SOAL: Second-order online active learning,” in Proceedings of the IEEE 16th International Conference on Data Mining (ICDM’16). IEEE, December 2016, pp. 931–936.
bibtex: 
@inproceedings{LILY-c106, 
    author	= {Hao, Shuji and Zhao, Peilin and Lu, Jing and Hoi, Steven C. H. and Miao, Chunyan and Zhang, Chi},
    title	= {{SOAL}: Second-order Online Active Learning},  
    booktitle	= {Proceedings of the  IEEE 16th International Conference on Data Mining (ICDM'16)}, 
    year		= {2016}, 
    month	= {December}, 
    pages	= {931-936}, 
    location	= {Barcelona, Spain},
    publisher	= {IEEE},
 }