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2000

A neural network approach to adaptive pattern analysis - the deformable feature map

12 years 7 months ago
A neural network approach to adaptive pattern analysis - the deformable feature map
Abstract. In this paper, we presen t 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 asubsequent, appropriate similarity transformation whic his based on a self-organized deformation of the underlying multidimensional probability distributions. After discussing the theory of the DM algorithm, w euse a computer sim ulation to visualize its e ects on a tw o-dimensional toy example. Finally, we presen t results of its application to the real-world problem of fully automatic voxel-based multispectral image segmentation, employing magnetic resonance data sets of the human brain.
Axel Wismüller, Frank Vietze, Dominik R. Ders
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where ESANN
Authors Axel Wismüller, Frank Vietze, Dominik R. Dersch, Klaus Hahn, Helge Ritter
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