Semi-supervised classification in stratified spaces by considering non-interior points using Laplacian behavior
- Submitting institution
-
Staffordshire University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6792
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2017.02.019
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 223-231
- Volume
- 239
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Deposit exception
- Month of publication
- May
- Year of publication
- 2017
- URL
-
http://doi.org/10.1016/j.neucom.2017.02.019
- 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
-
1
- Research group(s)
-
B - Centre for Smart Systems, AI and Cybersecurity (CSSAIC)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Semi-supervised classification is important in applications where large numbers of data points are unlabelled due to the cost or difficulty of labelling data, e.g., computer aided diagnosis, drug discovery. The significance of this paper is that it overcomes the problem of over learning of locality found in most existing solutions. Based on this and a subsequent paper the first author, Karimi, was appointed as an Assistant Professor at Damghan University, Iran.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -