TR available in Neuroprose Archives

[desa@cs.rochester.edu]

The following technical report has been placed in the neuroprose archive:


              Top-down teaching enables non-trivial clustering 
			via competitive learning

	         Virginia de Sa          Dana Ballard
		desa at cs.rochester.edu   dana at cs.rochester.edu

			 Dept. of Computer Science
		      	  University of Rochester
			  Rochester, NY 14627-0226


				Abstract:

   Unsupervised competitive learning classifies patterns based on
   similarity of their input representations.  As it is not
   given external guidance, it has no means of incorporating task-specific
   information useful for classifying based on semantic similarity.
   This report describes a method of augmenting the basic competitive
   learning algorithm with a top-down teaching signal.   This teaching signal
   removes the restriction inherent in unsupervised learning and allows
   high level structuring of the representation while maintaining
   the speed and biological plausibility of a local Hebbian style learning
   algorithm.  Examples, using this algorithm in small
   problems, are presented and the function of the teaching input
   is illustrated geometrically.  This work supports the hypothesis
   that cortical back-projections are important for the organization of
   sensory traces during learning.

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