Learning schemes such as Competitive Learning and
Learning Vector Quantization (LVQ) are based on
the representation of data by appropriately chosen
While intuitively clear and widely used in a variety
of classification problems, most algorithms of LVQ
are heuristically motivated and lack, for instance,
the relation to a well-defined cost function.
Nevertheless, methods borrowed from Statistical Physics
allow for a systematic study of such learning processes.
Model situations in which the training is based on
high-dimensional, randomized data can be studied
analytically. It is possible, for instance, to
compute typical learning curves, i.e. the success of
learning vs. the number of example data.
Besides the analysis and comparison of standard algorithms,
the aim of these studies is to devise novel, more efficient
This talk summarizes our recent results concerning
several unsupervised and supervised schemes of Vector
Quantization and gives an outlook on forthcoming projects.
Furthermore, we would greatly appreciate suggestions
concerning potential applications of LVQ training.
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