Refereed full papers (journals, book chapters, international conferences)

2010

@inproceedings{ZaS2010a,
	vgclass =	{refpap},
	author =	{Zaidi, Nayyar Abbas and David McG.\ Squire},
	title =	{{SVM}s and Data Dependent Distance Metric},
	booktitle =	{Proceedings of the 25th International Conference on Image
	and Vision Computing New Zealand},
	address =	{Queenstown, New Zealand},
	pages =	{265--271},
	publisher =	{Institute of Electrical and Electronics Engineers (IEEE)},
	month =	{November~8--9},
	year =	{2010},
	doi =	{http://dx.doi.org/10.1109/IVCNZ.2010.6148826},
	abstract =	{Support Vector Machine (SVM) is an efficient classification
	tool. Based on the principle of structured risk minimization, SVM is
	designed to generalize well. But it has been shown that SVM is not
	immune to the curse of dimensionality. Also SVM performance is not only
	critical to the choice of kernel but also kernel parameters which are
	generally tuned through computationally expensive cross-validation
	procedures.  Typical kernels do not have any information about the
	subspace to ignore irrelevant features or making relevant features
	explicit. Recently, a lot of progress has been made for learning a data
	dependent distance metric for improving the efficiency of k-Nearest
	Neighbor (KNN) classifier. Metric learning approaches have not been
	investigated in the context of SVM. In this paper, we study the impact
	of learning a data dependent distance metric on classification
	performance of an SVM classifier. Our novel approach in this paper is a
	formulation relying on a simple Mean Square Error (MSE) gradient based
	metric learning method to tune kernel's parameters. Experiments are
	conducted on major UCIML, faces and digit databases. We have found
	that tuning kernel parameters through metric learning approach can
	improve the classification performance of an SVM classifier.},
}