Search results for key=ZaS2010 : 1 match found.

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.},
  }