A geometric framework for data fusion in information retrieval
- Submitting institution
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University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76447813
- Type
- D - Journal article
- DOI
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10.1016/j.is.2015.01.001
- Title of journal
- Information Systems
- Article number
- -
- First page
- 20
- Volume
- 50
- Issue
- -
- ISSN
- 0306-4379
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- 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
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1
- Research group(s)
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B - Artificial Intelligence Research Centre
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <24> This paper has inspired two researchers, Hamed R. Bonab and Fazli Can from the Middle East Technical University (METU) in Turkey, to set up the geometric framework for the classification problem in machine learning. They developed their work based on the four theorems developed in this paper and used them to establish a new assertion. Their work was published in the paper “Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers” in IEEE Transactions on Neural Networks and Learning Systems (DOI: 10.1109/TNNLS.2018.2886341) in 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -