| EPSRC Reference: |
GR/N07394/01 |
| Title: |
EFFICIENT FIRST-ORDER PROBABILISTIC MODELS FOR INFERENCE AND LEARNING |
| Principal Investigator: |
Flach, Professor P |
| Other Investigators: |
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| Project Partners: |
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| Department: |
Computer Science |
| Organisation: |
University of Bristol |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 October 2000 |
Ends: |
30 September 2003 |
Value (£): |
51,360
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Information Technologies |
No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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A probalilistic model is any formalism to specify a complex probability distribution. Such formalisms facilitate uncertainty handling and evidential reasoning in artificial intelligence. Current probabilistic models restrict their variables to simple boolean propositions, discrete attributes, or numbers. The goal of this project is to enhance these models with the power of first-order logic. This enables the variables to range over complex structured objects, be they molecules or websites. The project proposes new methods for specifying such models, reasoning with them, and learning them from data. The approach uses the individual-centred respresenatations that are a central topic of sutdy in recent work in machine learning and inductive logic programming. Possible domains of application include molecular biology, drug design, information retrieval on the web, and user modelling. Experimental validation of the utility of first-order probabilistic models will be carried on some of these domains.
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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.bris.ac.uk |