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CEAL

Description: This package provides the implementation of CEAL method. CEAL is a cost-effective active learning method for crowdsourcing setting, where multiple labelers are available to offer diverse qualities of labeling with different costs. A readme file is included in the package.

References:
Sheng-Jun Huang, Jia-Lve Chen, Xin Mu and Zhi-Hua Zhou. Cost-Effective Active Learning from Diverse Labelers. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 2017.



ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).

Requirement: This package is developed with MATLAB, and it should be run on Windows. We employ "liblinear" for classification model, please make sure "liblinear" is installed.

ATTN2: This package was developed by Mr. Jia-Lve Chen, Mr. Xin Mu and Dr. Sheng-Jun Huang (huangsj@lamda.nju.edu.cn). For any problem concerning the codes, please feel free to contact Dr. Huang.

Download: [code] (200Kb)

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