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Details of Grant 

EPSRC Reference: GR/M56067/02
Title: CLOSED LOOP MACHINE LEARNING
Principal Investigator: Muggleton, Professor S
Other Investigators:
Kell, Professor DB Oliver, Professor SG King, Professor R
Researcher Co-Investigators:
Project Partners:
Department: Computing
Organisation: Imperial College London
Scheme: Standard Research (Pre-FEC)
Starts: 01 September 2001 Ends: 31 March 2002 Value (£): 52,213
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:  
Summary on Grant Application Form
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.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
Date Materialised
Sectors submitted by the Researcher
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Project URL:  
Further Information:  
Organisation Website: http://www.imperial.ac.uk