| dc.contributor.author | Lane, P.C.R. | |
| dc.contributor.author | Gobet, F. | |
| dc.date.accessioned | 2011-04-07T11:25:28Z | |
| dc.date.available | 2011-04-07T11:25:28Z | |
| dc.date.issued | 2005 | |
| dc.identifier.citation | Lane , P C R & Gobet , F 2005 , ' Discovering predictive variables when evolving cognitive models ' , Lecture Notes in Computer Science (LNCS) , no. 3rd Int Conf on Advances in Pattern Recognition , pp. 108-117 . https://doi.org/10.1007/11551188_12 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.other | dspace: 2299/5575 | |
| dc.identifier.uri | http://hdl.handle.net/2299/5575 | |
| dc.description | “The original publication is available at www.springerlink.com”. Copyright Springer | |
| dc.description.abstract | A non-dominated sorting genetic algorithm is used to evolve models of learning from di erent theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence. | en |
| dc.format.extent | 131191 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Lecture Notes in Computer Science (LNCS) | |
| dc.title | Discovering predictive variables when evolving cognitive models | en |
| dc.contributor.institution | School of Computer Science | |
| dc.contributor.institution | Centre for Computer Science and Informatics Research | |
| dc.contributor.institution | Department of Computer Science | |
| dc.contributor.institution | School of Physics, Engineering & Computer Science | |
| dc.description.status | Peer reviewed | |
| rioxxterms.versionofrecord | 10.1007/11551188_12 | |
| rioxxterms.type | Journal Article/Review | |
| herts.preservation.rarelyaccessed | true | |
