| EPSRC Reference: |
GR/M56067/02 |
| Title: |
CLOSED LOOP MACHINE LEARNING |
| Principal Investigator: |
Muggleton, Professor S |
| Other Investigators: |
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| Project Partners: |
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| Department: |
Computing |
| Organisation: |
Imperial College London |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 September 2001 |
Ends: |
31 March 2002 |
Value (£): |
52,213
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Pharmaceuticals and Biotechnology |
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Summary on Grant Application Form |
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Machine learning systems that produce human- comprehensible hypotheses from the data are being increasingly used for knowledge discovery within both business and science. These systems are typically open loop, with no direct link between the Machine Learning systems. This project will test the alternative of Closed loop machine learning the system. This is related to the area of active learning in which the machine learning system actively selects experiments to discriminate between contending hypothesis. In the closed loop machine learning the system not only selects but also carries out these experiments in the learning domain.
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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.imperial.ac.uk |