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Details of Grant 

EPSRC Reference: GR/M56067/01
Title: CLOSED LOOP MACHINE LEARNING
Principal Investigator: Muggleton, Professor S
Other Investigators:
Oliver, Professor S Kell, Professor DB Oliver, Professor SG
King, Professor R
Researcher Co-Investigators:
Project Partners:
GlaxoSmithKline plc (GSK)
Department: Computer Science
Organisation: University of York
Scheme: Standard Research (Pre-FEC)
Starts: 16 March 1999 Ends: 31 August 2001 Value (£): 192,921
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.york.ac.uk