TR available in Neuroprose Archives
desa@cs.rochester.edu
desa at cs.rochester.edu
Wed Dec 11 17:25:03 EST 1991
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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