Symbolic vs Vector Space Learning

[Kamat]

Dear Connectionists,

It appears that many neural net researchers do not agree with the main
point of the paper [*] posted by me two days ago:

   that there is a fundamental difference between the **appropriate** 
   underlying mathematical models for neural nets and symbolic 
   learning machines.

Since one of us will be working on the formal proof of the above 
statement, and the issue is so critical that it might be useful
(to say the least) to discuss this issue on this mailing list.

Vithal

[*] L. Goldfarb, J. Abela, V. C. Bhavsar and V. N. Kamat, Can a vector
    space based learning model discover inductive class generalization 
    in a symbolic environment? (to be published in Pattern Recognition
    Letters).

============================================================================
 Vithal N. Kamat, PhD Student, AI group,  Faculty of Computer Science,
 University of New Brunswick, PO Box 4400, Fredericton, NB, E3B 5A3, CANADA.
 u095 at unb.ca. Fax:(506) 453-3566
 My URL is http://ccortex.cs.unb.ca:8080/~kamat/kamat.html.
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