More on AI vs. NN

[Tony Chan]

Jim Hendler [Thu, 20 Dec 90 09:18:09 EST] suggests four ways to endow a
system so that it can provide both "`low-level'' perceptual
functionality as well as demonstrating high-level cognitive abilities"
one of which is: "we can work out a new ``paradigm,'' yet another
competitor to enter the set of possible models for delivering so-called
intelligent behavior."

The following short paper belongs to that class.
 title    = "Learning as optimization: An overture",
 booktitle= "IASTED International Symposium on Machine Learning and
            Neural Networks",
 pages    = "100--103",
 address  = "New York",
 month    = "Oct 10--11",
 year     = 1990,
 Abstract =
There are two principal paradigms for the study of, pattern learning or
machine learning or simply learning.  The symbolic paradigm [high-level]
for learning, mainly of the AI approach, is typified by Mitchell's
version space model and Lenat's heuristic model. And the numeric
paradigm [low-level] is represented by the pattern recognition model and
the neural net model.  In this paper a unified paradigm based on an
extension of the Goldfarb's metric learning model is outlined.  The
unified learning paradigm prescribes a special type of optimization over
a parametric family of pseudometric (distance) spaces in order to
achieve a certain stability structure (stable configuration) in the
optimal pseudometric space which is an output of the learning procedure.
This special optimization procedure provides a mathematical guidance by
which a system learns to organize itself.