(Enter summary)
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... (Update)
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BibTeX entry: (Update)
Lowe, D. G. (1995). "Similarity metric learning for a variable-kernel classifier." Neural Computation, . http://citeseer.ist.psu.edu/lowe95similarity.html More
@techreport{ lowe93similarity,
author = "David G. Lowe",
title = "Similarity Metric Learning for a Variable-Kernel Classifier",
number = "TR-93-43",
year = "1993",
url = "citeseer.ist.psu.edu/lowe95similarity.html" }
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