| EPSRC Reference: |
GR/M81601/01 |
| Title: |
ANALYSIS OF LEARNING IN SUPPORT VECTOR MACHINES |
| Principal Investigator: |
Opper, Professor M |
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| Department: |
Sch of Engineering and Applied Science |
| Organisation: |
Aston University |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 March 2000 |
Ends: |
28 February 2002 |
Value (£): |
118,444
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Summary on Grant Application Form |
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Support-vector machines (SVMs) constitutes a new promising paradigm in neural computation and machine learning and are presently one of the most active topics in these research areas. SVMs are nonparametric approaches to supervised learning which avoid the necessity of an a priori specification of the number of adjustable parameters. Previous theoretical approaches to the generalisation ability of SVMs rely mainly on worst case, distribution independent bounds. The tightness of these estimates in less pessimistic, typical cases is not clear and the choice of a suitable SVM model based on them could lead to suboptimal performance. The goal of the proposed project is to apply techniques from statistical mechanics to study analytically the learning and generalisation performance of SVMs in typical, rather than worst case, scenarios. The method will not only yield exact results for various quantities of the trained SVM, but will also provide insights into SVM learning. Investigations of model selection for learning with noisy training data will provide invaluable insight for the application of SVMs to real world tasks from which new approaches may emerge. Finally, we will study the ability of SVM training algorithms to deal with large data sets as it is crucial for future applications.
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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.aston.ac.uk |