Search results for key=ZaS2010a : 1 match found.

Search my entire BibTeX database
Output format: Text
BibTeX entry
     Combine using:

Abstract icon Abstract BibTeX icon BibTeX entry Postscript icon Postscript PDF icon PDF PPT icon Powerpoint

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


  • @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 =	{},
    	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.},