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

1995

@article{Low1995,
	vgclass =	{refpap},
	author =	{David G. Lowe},
	title =	{Similarity metric learning for a variable-kernel
	classifier},
	journal =	{Neural Computation},
	volume =	{7},
	number =	{1},
	pages =	{72--85},
	month =	{January},
	year =	{1995},
	url =	{http://www.cs.ubc.ca/spider/lowe/papers/neural95/neural.html},
	url1 =	{http://www.cs.ubc.ca/spider/lowe/papers/neural95.ps},
	abstract =	{Nearest-neighbour interpolation algorithms have many
	useful properties for applications to learning, but they often exhibit
	poor generalization. In this paper, it is shown that much better
	generalization can be obtained by using a variable interpolation kernel
	in combination with conjugate gradient optimization of the similarity
	metric and kernel size. The resulting method is called variable-kernel
	similarity metric (VSM) learning. It has been tested on several
	standard classification data sets, and on these problems it shows
	better generalization than back propagation and most other learning
	methods. An important advantage is that the system can operate as a
	black box in which no model minimization parameters need to be
	experimentally set by the user. The number of parameters that must be
	determined through optimization are orders of magnitude less than for
	back-propagation or RBF networks, which may indicate that the method
	better captures the essential degrees of variation in learning. Other
	features of VSM learning are discussed that make it relevant to models
	for biological learning in the brain.},
}