Attribute selection in neural networks used to classify remotely sensed data Milne, Linda en_US 2021-11-25T12:46:38Z 2021-11-25T12:46:38Z 1997 en_US
dc.description.abstract As more remotely sensed data becomes available there is an increasing need for automated image processing techniques. In particular there is a need for the selection of relevant attributes used in a given classifcation problem. Neural networks are widely used for classifcation of image data, but few practitioners achieve optimal results. In part, this is due to the use of noisy or irrelevant data. This paper compares a new attribute selection method specifcally for use with neural networks, namely contribution analysis, with the more general wrapper method of attribute selection. en_US
dc.language English
dc.language.iso EN en_US
dc.publisher University of Sydney 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 Neural Networks, Genetic Algorithms and Fuzzy Logic (280212) en_US
dc.title Attribute selection in neural networks used to classify remotely sensed data en_US
dc.type Conference Paper en
dcterms.accessRights open access
dspace.entity.type Publication en_US
unsw.relation.faculty Engineering
unsw.relation.ispartofconferenceLocation Sydney en_US
unsw.relation.ispartofconferenceName Visual Information Processing Workshop en_US
unsw.relation.ispartofconferenceProceedingsTitle Proceedings of the First Visual Information Processing Workshop en_US
unsw.relation.ispartofconferenceYear 1997 en_US
unsw.relation.ispartofpagefrompageto 21-26 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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