Description: QUIRE is a package for active learning by querying informative and representative examples. QUIRE selects unlabeled instances
that are both informative and representative based on the min-max view of active learning, which provides a systematic way for measuring and combining the informativeness and the representativeness. Both the single-label and multi-label versions are included.
Reference:
[1] S.-J. Huang, R. Jin and Z.-H. Zhou.
Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems 24 (NIPS'10), Vancouver, Canada, 2010.
[2] S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.
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 .
Requirement: The package was developed with MATLAB 2008.
ATTN2: This package was developed by Mr. Sheng-Jun Huang (huangsj@lamda.nju.edu.cn). For any problem concerning the code, please feel free to contact Mr. Huang.
Download: code (3KB)