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  • @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 =	{},
    	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.},