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Soft Competitive Learning with Fixed Network Dimensionality

  In this chapter methods from the area of soft competitive learning are described which have a network of a fixed dimensionality k which has to be chosen in advance. One advantage of a fixed network dimensionality is that such a network defines a mapping from the n-dimensional input space (with n being arbitrarily large) to the k-dimensional structure. This makes it possible to get a low-dimensional representation of the data which may be used for visualization purposes.

Bernd Fritzke
Sat Apr 5 18:17:58 MET DST 1997