Deep-FS: a feature selection algorithm for Deep Boltzmann Machines
- Submitting institution
-
Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1973
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2018.09.040
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 22
- Volume
- 322
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Technical exception
- Month of publication
- September
- Year of publication
- 2018
- 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
-
2
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work led to the capture of industrial grant funding. Cundall Ltd and Railston & Co each invested £40,000 to co-fund PhD students supervised by Dr Cosma, to develop deep feature selection/classification algorithms to: identify predictors of energy ratings from large-scale building inspection reports; and analyse large sets of wind turbine and train wheel images, respectively.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -