Feature selection using neural networks with contribution measures Milne, Linda en_US 2021-11-25T12:46:42Z 2021-11-25T12:46:42Z 1995 en_US
dc.description.abstract There still seems to be a misapprehension that neural networks are capable of dealing with large amounts of noise and useless data. This is true to a certain extent but it is also true that the cleaner and more descriptive the data is the better the neural networks will perform, especially when dealing with small data sets. A method for determining how useful input features are in giving correct classifcations using neural networks is discussed here. en_US
dc.description.uri en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.subject.other contribution analysis en_US
dc.subject.other neural network en_US
dc.subject.other attribute selection en_US
dc.subject.other noisy data en_US
dc.subject.other irrelevant data en_US
dc.subject.other small datasets en_US
dc.subject.other Neural Networks, Genetic Algorithms and Fuzzy Logic (280212) en_US
dc.title Feature selection using neural networks with contribution measures en_US
dc.type Conference Paper en
dcterms.accessRights open access
dspace.entity.type Publication en_US
unsw.relation.faculty Engineering
unsw.relation.ispartofconferenceLocation Canberra, Australia en_US
unsw.relation.ispartofconferenceName Eighth Australian Joint Conference on Artificial Intelligence, AI’95 en_US
unsw.relation.ispartofconferenceYear 1995 en_US
unsw.relation.originalPublicationAffiliation Milne, Linda, Computer Science & Engineering, Faculty of Engineering, UNSW en_US School of Computer Science and Engineering *
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