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Soft Learning Vector Quantization
Citation key Seo2003b
Author Seo, S. and Obermayer, K.
Pages 1589 – 1604
Year 2003
DOI 10.1162/089976603321891819
Journal Neural Computation
Volume 15
Number 7
Publisher MIT Press
Abstract 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.
Bibtex Type of Publication Selected:quantization
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