| EPSRC Reference: |
GR/L35812/01 |
| Title: |
SUPPORT VECTOR AND BAYESIAN LEARNING ALGORITHMS: ANALYSIS AND APPLICATIONS |
| Principal Investigator: |
Gammerman, Professor A |
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| Department: |
Computer Science |
| Organisation: |
Royal Holloway, Univ of London |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 August 1997 |
Ends: |
30 November 2000 |
Value (£): |
143,187
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Summary on Grant Application Form |
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Much research has been devoted to the study of various learning algorithms. Currently, the most popular approaches to machine learning are the Bayesian approach and the so-called best-model approach (based on several different inductive principles such as structural risk minimisation). The principal disadvantage of both approaches is their relative computational inefficiency (the curse of dimensionality). The project aims to develop computationally efficient algorithms in order to overcome this problem and to compare the approaches in their predictive power. To validate the algorithms they will be applied to several real-life data sets.
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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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