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

2010

@inproceedings{ZaS2010,
	vgclass =	{refpap},
	author =	{Zaidi, Nayyar Abbas and David McG.\ Squire},
	title =	{Local Adaptive {SVM} for Object Recognition},
	booktitle =	{Proceedings of the International Conference on Digital
	Image Computing: Techniques and Applications (DICTA 2010)},
	address =	{Sydney, Australia},
	month =	{December~1--3},
	year =	{2010},
	doi =	{http://dx.doi.org/10.1109/DICTA.2010.44},
	abstract =	{The Support Vector Machine (SVM) is an effective
	classification tool.  Though extremely effective, SVMs are not a
	panacea. SVM training and testing is computationally expensive.  Also,
	tuning the kernel parameters is a complicated procedure. On the other
	hand, the Nearest Neighbor (KNN) classifier is computationally
	efficient. In order to achieve the classification efficiency of an SVM
	and the computational efficiency of a KNN classifier, it has been shown
	previously that, rather than training a single global SVM, a separate
	SVM can be trained for the neighbourhood of each query point. In this
	work, we have extended this Local SVM (LSVM) formulation. Our Local
	Adaptive SVM (LASVM) formulation trains a local SVM in a modified
	neighborhood space of a query point.  The main contributions of the
	paper are twofold: First, we present a novel LASVM algorithm to train a
	local SVM. Second, we discuss in detail the motivations behind the LSVM
	and LASVM formulations and its possible impacts on tuning the kernel
	parameters of an SVM.  We found that training an SVM in a local
	adaptive neighborhood can result in significant classification
	performance gain. Experiments have been conducted on a selection of the
	UCIML, face, object, and digit databases.},
}