No subject

Regarding the panel discussion on "connectionist learning : is it
time to reconsider the foundations ?", I read the arguments with
interest and I would like to pose, in addition, another issue.

That is, to simulate convincingly a biological system we should
not probably be dealing solely with vectors of real numbers. The
capacity to deal with symbols and other types of data merits also
attention. In other words, besides memory and more global
learning capabilities, it will be advantageous to be able to
handle jointly disparate data such as real numbers, fuzzy sets,
propositional statements, etc.

Therefore from a model development point of view it might be
quite advantageous to consider working on less structured
spaces than the conventional N-dimensional Euclidean space.
Such a space could be a (mathematical) lattice. Note that all
the previously mentioned data are in effect elements of a
mathematical lattice. That is, not only the conventional
Euclidean space is a lattice but also the set of propositional
statements, the collection of fuzzy sets on a universe of
discourse, etc.

My tentative proposion is this : For machine learning purposes
only, replace the Euclidean-space by a Lattice-space.

Just imagine how much the learning and decision making robustness
of a system would be enhanced if in addition to memory, the
capability to design the net on its own, polynomial complexity,
generalization capability, etc., the system in question in also
able to handle jointly dsparate data.

 With considerations,

Vassilis G. Kaburlasos
Aristotle University of Thessaloniki, Greece

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