Hierarchical Subquery Evaluation for Active Learning on a Graph
- Submitting institution
-
The University of Bath
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 146393555
- Type
- E - Conference contribution
- DOI
-
10.1109/CVPR.2014.79
- Title of conference / published proceedings
- CVPR '14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition
- First page
- 564
- Volume
- -
- Issue
- -
- ISSN
- 1063-6919
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper received an oral presentation at the top competetive computer vision venue (CVPR) and was notable for providing a practical method (computationally tractable) to combine the use of transductive graph-based active learning methods and the desirable expected error reduction metric; this combination was known to offer advantageous theoretical properties but was previously too computationally expensive for practical applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -