Item

Nominal-scale evolving connectionist systems

Watts, Michael J.
Date
2006-07
Type
Conference Contribution - published
Collections
Bio-Protection Research Centre
Keywords
orthogonal encoding, learning (artificial intelligence), neural nets, binary encoding, data mining, evolving connectionist system algorithm, nominal-scale data
Fields of Research
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.
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Name: IJCNN.2006.246974.pdf
Size: 108.91 KB
Kind: Adobe PDF
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https://hdl.handle.net/10182/1932
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© 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.
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