Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2009.5138876
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dc.titleCombining a global SVM and local nearest-neighbor classifiers driven by local discriminative boundaries
dc.contributor.authorXiong, W.
dc.contributor.authorOng, S.H.
dc.contributor.authorLe, T.T.
dc.contributor.authorLim, J.H.
dc.contributor.authorLiu, J.
dc.contributor.authorFoong, K.
dc.date.accessioned2014-06-19T03:02:57Z
dc.date.available2014-06-19T03:02:57Z
dc.date.issued2009
dc.identifier.citationXiong, W.,Ong, S.H.,Le, T.T.,Lim, J.H.,Liu, J.,Foong, K. (2009). Combining a global SVM and local nearest-neighbor classifiers driven by local discriminative boundaries. 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 : 3597-3600. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICIEA.2009.5138876" target="_blank">https://doi.org/10.1109/ICIEA.2009.5138876</a>
dc.identifier.isbn9781424428007
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69635
dc.description.abstractNonlinear support vector machines (SVMs) rely on the kernel trick and tradeoff parameters to build nonlinear models to classify complex problems and balance misclassification and generalization. The inconvenience in determining the kernel and the parameters has motivated the use of local nearest neighbor (NN) classifiers in lieu of global classifiers. This substitution ignores the advantage of SVM in global error minimization. On the other hand, the NN rule assumes that class conditional probabilities are locally constant. Such an assumption does not hold near class boundaries and in any high dimensional space due to the curse of dimensionality. We propose a hybrid classification method combining the global SVM and local NN classifiers. Local classifiers occur only when the global SVM is likely to fail. Furthermore, local NN classifiers adopt an adaptive metric driven by local SVM discriminative boundaries. Improved performance has been demonstrated compared to partially similar. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICIEA.2009.5138876
dc.sourceScopus
dc.subjectAdaptive metric
dc.subjectBoundary driven
dc.subjectCombination
dc.subjectLocal
dc.subjectNearest neighbors
dc.subjectSupport vector machines
dc.typeConference Paper
dc.contributor.departmentPREVENTIVE DENTISTRY
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICIEA.2009.5138876
dc.description.sourcetitle2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
dc.description.page3597-3600
dc.identifier.isiutNOT_IN_WOS
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