Paper on topological semantics

[Michael J. Healy 425-865-3123]

This is now out in the latest issue of Connection Science.  I have a 
supply of reprints and will be glad to send one to anyone who is 
interested:

M. J. Healy (1999)
"A Topological Semantics for Rule Extraction with Neural Networks", 
Connection Science, vol. 11, no. 1, pp. 91-113.

This is the paper I referred to in the Connectionists on-line discussion 
on connectionist symbol processing and in the follow-up issue of Neural 
Computing Surveys.  It introduces a mathematical approach to analyzing 
the semantics of neural networks and their applications.  It surveys the 
point-set-topology version of the approach; a more involved category-
theoretic version is intended for a later paper.  

The main example given in this paper concerns the adequacy of a specific 
neural network architecture for rule extraction.  Items discussed in 
addition to the mathematical background include issues in learning from 
data, specialization hierarchies, the concept of a prototype data object, 
continuous functions and rule-based systems, formal verification, and 
how the topological approach might be applied to neural network analysis 
and design.

Mike
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