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Abstract:
We present a new approach to the supervised learning of
lateral interactions for the competitive layer model (CLM)
dynamic feature binding architecture. The method is based on
consistency conditions, which were recently shown to characterize
the attractor states of this linear threshold recurrent network.
For a given set of training examples the learning problem is
formulated as a convex quadratic optimization problem in the
lateral interaction weights. An efficient dimension reduction of
the learning problem can be achieved by using a linear
superposition of basis interactions. We show the successful
application of the method to a medical image segmentation problem
of fluorescence microscope cell images.
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