Nonlinear identification using orthogonal forwardregression with nested optimal regularization
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 39716
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2015.2389524
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 2925
- Volume
- 45
- Issue
- 12
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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)
-
8 - CV
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Cross validation and regularization are fundamental concepts for sparse modelling and model generalization. Previous works have not considered optimization of local regularization parameters based on leave one out error rate analytically. The significance of this paper is that it represents the first work in constructing and optimizing locally regularisation parameters for radial basis neural networks in forward regression.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -