Fuzzy Transfer Learning: Methodology and application
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11187
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2014.09.004
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 59
- Volume
- 293
- Issue
- -
- ISSN
- 0020-0255
- 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
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1
- Research group(s)
-
-
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has formed the backbone of further research in transfer learning through the development and application of the methodology in areas such as healthcare. The paper has been well received by other academics [Witold Pedrycz (Canadian Research Chair, IEEE Fellow) alongside Hua Zuo have focussed on the use of FuzzyTL]. The methodology of Fuzzy Transfer Learning (FuzzyTL) has been adapted to incorporate Takagi-Sugeno models and been applied to a number of applications including: label space adaptation and data shortage.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -