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- S. Seo and K. Obermayer.
Soft Learning Vector Quantization.
.
Neural Comput., 15:1589-1604, 2003.
(FTP PostScript, 13 pages, 582 kb)
Learning Vector Quantization is a popular class of adaptive nearest
prototype classifiers for multiclass classification, but learning algorithms
from this family have so far been proposed on heuristic grounds. Here we take
a more principled approach and derive two variants of Learning Vector
Quantization using a Gaussian mixture ansatz. We propose an objective
function which is based on a likelihood ratio and we derive a learning rule
using gradient descent. The new approach provides a way to extend the
algorithms of the LVQ family to different distance measure and allows for the
design of ``soft Learning Vector Quantization algorithms. Benchmark
results show that the new methods lead to better classification performance
than LVQ 2.1. An additional benefit of the new method is that model
assumptions are made explicit, so that the method can be adapted more easily
to different kinds of problems.
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