paper in neuroprose

[Volker Tresp]

The following paper has been placed in the neuroprose archive 
as  tresp.rules.ps.Z
Instructions for retrieving and printing follow the abstract.



-----------------------------------------------------------------

NETWORK STRUCTURING AND TRAINING USING RULE-BASED KNOWLEDGE

-----------------------------------------------------------------



Volker Tresp, 		Siemens, Central Research

Juergen Hollatz, 	TU Muenchen

Subutai Ahmad, 		Siemens, Central Research




Abstract


We  demonstrate in this paper how certain forms of rule-based 
knowledge can be used to prestructure a  neural network of
normalized basis functions  and give a probabilistic
 interpretation of the  network architecture.  We describe several 
ways to assure that rule-based knowledge is  preserved during 
training and  present a method for complexity reduction that
tries to minimize the number of rules
and the number of conjuncts. After training,
the refined rules are extracted and analyzed. 


To appear in:

S. J. Hanson, J. D. Cowan, and C. L. Giles (Eds.), Advances in Neural
Information Processing Systems 5. San Mateo CA: Morgan Kaufmann.


----
Volker Tresp
Siemens AG, Central Research,   		Phone: 	+49 89 636-49408
Otto-Hahn-Ring 6,                            	FAX: 	+49 89 636-3320
W-8000 Munich 83, Germany           	  	E-mail: tresp at zfe.siemens.de




     unix> ftp archive.cis.ohio-state.edu (or 128.146.8.52)
     Name: anonymous
     Password: neuron
     ftp> cd pub/neuroprose
     ftp> binary
     ftp> get tresp.rules.ps.Z
     ftp> quit
     unix> uncompress  tresp.rules.ps.Z
     unix> lpr -s tresp.rules.ps   (or however you print postscript)