Please use this identifier to cite or link to this item:
|Title:||Combining a global SVM and local nearest-neighbor classifiers driven by local discriminative boundaries|
Support vector machines
|Citation:||Xiong, 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. https://doi.org/10.1109/ICIEA.2009.5138876|
|Abstract:||Nonlinear 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.|
|Source Title:||2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 19, 2019
checked on Dec 15, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.