motives for RBF networks

[JOHN K. KRUSCHKE]

Regarding uses of radial basis functions (RBFs) in neural networks:

One motive for using RBFs has been the promise of better interpolation
between training examples (i.e., better generalization). 

Some suggest that RBF nodes are also neurally plausible (at least in
the type of function they compute, if not the methods used to train
them). 

(See previous postings by other Connectionists for references.) 

Another motive comes from the molar, psychological level.  In some
situations, human behavior can be accurately described in terms of
memory for specific exemplars, with generalization to novel exemplars
based on similarity to memorized exemplars.  If an exemplar is encoded
as a point in a multi-dimensional psychological space, then an
internally memorized exemplar can be represented by an RBF node
centered on that point.  When combined with back-propagation learning
(and learned dimensional attention strengths), such RBF-based networks
can do a reasonably good job of capturing human performance in several
category learning tasks. 
 

Some references:

Kruschke, J. K. (1991a).
ALCOVE: A connectionist model of human category learning.
In: R. P. Lippmann, J. E. Moody & D. S. Touretzky (eds.),
Advances in Neural Information Processing Systems 3,
pp.649-655.  San Mateo, CA: Morgan Kaufmann.
(Several other papers in this volume address related issues.)

Kruschke, J. K. (1991b).
Dimensional attention learning in models of human categorization.
In: Proceedings of the Thirteenth Annual Conference of the Cognitive 
Science Society, pp.281-286.  Hillsdale, NJ: Erlbaum.

Kruschke, J. K. (1991c).
Dimensional attention learning in connectionist models of human 
categorization.  
Indiana University Cognitive Science Research Report 50.

Kruschke, J. K. (in press).
ALCOVE: An exemplar-based connectionist model of category learning.
Psychological Review.  Scheduled to appear in January 1992.
[Indiana University Cognitive Science Research Report 47.]

Nosofsky, R. M.,  Kruschke, J. K. & McKinley, S. (in press).
Combining exemplar-based category representations and connectionist 
learning rules.  Journal of Experimental Psychology:  Learning, Memory 
and Cognition.  Scheduled to appear in March 1992.