| EPSRC Reference: |
GR/L59801/01 |
| Title: |
ROPA: NOVEL APPROACHES TO OPTIMISED SELF-CONFIGURATION IN HIGH PERFORMANCE MULTIPLE-EXPERT CLASSIFIERS |
| Principal Investigator: |
Fairhurst, Professor MC |
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| Department: |
Sch of Engineering & Digital Arts |
| Organisation: |
University of Kent |
| Scheme: |
ROPA |
| Starts: |
27 February 1998 |
Ends: |
26 February 2000 |
Value (£): |
97,507
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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A hierachical (serial/parallel hybrid) multiple expert pattern classifier architecture has been shown to offer high performance in various classification tasks. However, such structures require careful configuration for a specific task or when the pattern environment changes, and this can ve very difficult to accomplish without exhaustive experimentation. This project will investigate the requirements for optimisation within the hierachical structure and will develop both a novel empirical method for self-configuration (based on a constrained evoloutionary model) and a more formal framework which seeks to unify optimisation approaches across many different architectures.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.kent.ac.uk |