No subject

Chapter 1 of A New Approach to Pattern Recognition, in Progress in
Pattern Recognition 2, eds. L.N.Kanal and A.Rosenfeld, North-Holland,
1985) that the adoption of the VECTOR REPRESENTATION for the objects
severely limits the number of the above similarity fields that can be
induced naturally in the set of objects.

At the same time, it is also useful to remember that the limitations
imposed by the vector representation were sufficient to justify the rift
between AI an pattern recognition (this is not to say that I am condoning
this rift, which was also "politically" motivated). It is not difficult
to understand why vector representation is not sufficiently flexible: all
features are rigidly fixed, quantitative, and their interrelations are
not represented. In reality, the useful features and their relations must
emerge dynamically during the learning processes. "Symbolic"
representations such as strings, graphs, etc. are more satisfactory from
that point of view.

Thus, although the NN after the learning process can induce some
similarity field in the set of patterns, its capacity to generate various
similarity fields is SEVERELY RESTRICTED by the very form of the pattern
(object) representation. Furthermore, adapting a new more dynamic
framework for the NN (dynamic NNs) will solve only a small part of above
representational problem. The issue of representation have received
considerable attention in computer science, but, it appears, that people
trained in other fields may not fully appreciate its role and importance.

-- Lev Goldfarb