Abstract
In this paper, we present an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate similarity transformation which is based on a self-organized deformation of the underlying multi-dimensional probability distributions. After discussing the theory of the DM algorithm, we use computer simulations to visualize its effects on low-dimensional toy examples. Finally, we present results of its application to the real-world problem of automatic nonlinear multispectral image registration, employing magnetic resonance data sets of the human brain.
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