Search results for key=ZaS2010a : 1 match found.

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.},
  }