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- S. Seo, M. Bode, and
K. Obermayer. Soft Nearest Prototype Classification.
.
IEEE Trans. Neur. Netw., 14:390-398, 2003.
(FTP PostScript, 12 pages, 446 kb)
We propose a new method for the construction of nearest prototype
classifiers which is based on a Gaussian mixture ansatz and which can be
interpreted as an annealed version of Learning Vector Quantization. The
algorithm performs a gradient descent on a cost-function minimizing the
classification error on the training set. We investigate the properties of
the algorithm and asses its performance fo several toy data sets and for an
optcal letter classification task. Results sho i) that annealing in the
dispersion parameter of the Gaussian kernels improves classification
accuracy, ii) that classification resuolts are better than those obtained
with standard Learning Vector Quantization (LVQ 2.1, LVQ 3) for equal numbers
of prototypes and iii) that annealing of the width paramter improved the
classification capability. Additionally, the principled approach provides an
explanation of a number of features ofthe (heuristic) LV
methods.
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