| EPSRC Reference: |
GR/M95882/01 |
| Title: |
NOVEL MULTIPLE-EXPERT CLASSIFIERS FOR HANDWRITTEN DATA ANALYSIS |
| Principal Investigator: |
Fairhurst, Professor MC |
| Other Investigators: |
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| Department: |
Sch of Engineering & Digital Arts |
| Organisation: |
University of Kent |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 March 2000 |
Ends: |
31 March 2003 |
Value (£): |
179,265
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Summary on Grant Application Form |
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The traditional approach to the specification of high-performance practical classification systems has been to focus on the implementation of powerful, but expensive, individual experts . There is, however, a growing interest in the specification of systems based on the integration of multiple experts to improve attainable performance. Our previous work has investigated various multiple expert classifier architectures based on hierarchical structuring which offers significant performance enhancement. The proposal will build on these foundations to further develop these structures with a view to practical exploitation and to investigate a number of key issues both theoretical and practical, which underpin the specification of novel classifier structures offering a high performance and increased flexibility for multi-task applications. Issues relating to optimisation and implementation efficiency will also be investigated. Although these structures are generic, they will be evaluated in the task domain of handwritten data analysis and document processing, an application area of increasing significance.
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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 |