Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/1026
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dc.contributor.authorMoorgas, Kevin Emanuelen_US
dc.contributor.authorPillay, Nelendranen_US
dc.contributor.authorGovernder, Poobalanen_US
dc.date.accessioned2014-05-26T12:25:25Z
dc.date.available2014-05-26T12:25:25Z
dc.date.issued2012-08-
dc.identifier.citationGovender, P., Pillay, N., Moorgas, K.E. 2012. ANN's vs. SVM's for Image Classification. International Conference on Electrical and Computer Systems Ottawa: Internationa ASET.en_US
dc.identifier.urihttp://hdl.handle.net/10321/1026-
dc.description.abstractIn this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.en_US
dc.format.extent9 pen_US
dc.language.isoenen_US
dc.publisherInternational ASETen_US
dc.subjectHyperplaneen_US
dc.subjectSupport vector machineen_US
dc.subjectArtificial neural networken_US
dc.subjectPrinciple component analysisen_US
dc.subject.lcshSupport vector machinesen_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.subject.lcshPrincipal components analysisen_US
dc.titleANN’s vs. SVM’s for image classificationen_US
dc.typeArticleen_US
dc.dut-rims.pubnumDUT-002208en_US
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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