PRASAD@RED.RUTGERS.EDU (03/18/86)
Machine Learning Colloquium
LEARNING ARGUMENTS OF INVARIANT FUNCTIONAL DESCRIPTIONS
Mieczyslaw M. Kokar
Northeastern University
360 Huntington Avenue
Boston, MA 02115
11 AM, March 25, Tuesday
#423, Hill Center
The main subject of this presentation is discovery of concepts from
observation. The focus is on a special kind of concepts - arguments of
functional descriptions. The functions considered here are to be
meaningful, i.e., computable functions expressed in terms of the operations
defining the representation language in which the concepts are described.
Such functions are invariant under transformations of the representation
language into equivalent representations.
It will be shown that the feature of invariance can be utilized in
formulating and testing hypotheses about relevance of arguments of functional
descriptions. The main point is that the arguments do not need to be changed
to test the relevance. This is very important to the discovery process as the
arguments to be discovered are not known, therefore, how could they be
controlled?
Simple examples of discovering concepts of physical parameters (arguments
of physical laws) will be discussed.
** We need a host for the speaker. Please send me a message soon if you want to
host him.
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