Building Support Vector Machines in the Context of Regularised Least Squares
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 79099205
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2016.03.087
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 1
- Volume
- 211
- Issue
- 26
- ISSN
- 0925-2312
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2016
- 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)
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C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper contributed innovative intelligent classification techniques to the EU HaptimapHaptiMap project (FP7-ICT-224675) project toolkit. The context aware algorithms we developed were the first of their kind for iPhone and Android digital maps. The toolkit itself has been used in commercial apps (with >15,000 users) and user trial outcomes helped to formulate updated OGC standards, www.ogc.org, which allow software developers to create solutions where location information and services are required.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
