Nominal-scale evolving connectionist systems

Watts, Michael J.

Nominal-scale evolving connectionist systems

dc.contributor.author	Watts, Michael J.
dc.date.accessioned	2010-05-25T01:43:21Z
dc.date.issued	2006-07
dc.description.abstract	A method is presented for extending the evolving connectionist system (ECoS) algorithm that allows it to explicitly represent and learn nominal-scale data without the need for an orthogonal or binary encoding scheme. Rigorous evaluation of the algorithm over benchmark data sets shows that it is able to learn, generalise and adapt well to classification problems. The algorithm is potentially useful for data mining tasks.	en
dc.format.extent	4057-4061	en
dc.identifier.citation	Watts, M. J. (2006). Nominal-scale evolving connectionist systems. In 2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21, 2006 (pp. 2055-2059). Piscataway, NJ: IEEE.
dc.identifier.isbn	0-7803-9490-9	en
dc.identifier.uri	https://hdl.handle.net/10182/1932
dc.language.iso	en
dc.publisher	IEEE
dc.relation	The original publication is available from - IEEE	en
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.source	International Joint Conference on Neural Networks (IJCNN) 2006	en
dc.subject	orthogonal encoding	en
dc.subject	learning (artificial intelligence)	en
dc.subject	neural nets	en
dc.subject	binary encoding	en
dc.subject	data mining	en
dc.subject	evolving connectionist system algorithm	en
dc.subject	nominal-scale data	en
dc.title	Nominal-scale evolving connectionist systems	en
dc.type	Conference Contribution - published
lu.contributor.unit	Lincoln University
lu.contributor.unit	Bio-Protection and Ecology
lu.subtype	Conference Paper	en
pubs.finish-date	2006-07-21	en
pubs.publication-status	Published	en
pubs.start-date	2006-07-17	en