Applying Category Theory to Improve ...

[mjhealy@ece.unm.edu]

Our full account of an application of colimits and limits to improving
upon a standard neural architecture is soon to appear in the journal
Neurocomputing.  In case this interests you, a preprint is obtainable from
my website, http://www.ece.unm.edu/~mjhealy , or else contact me for a
copy.
The blurb:

Applying Category Theory to Improve the Performance of a Neural Architecture
Michael J. Healy, Richard D. Olinger, Robert J. Young,
Shawn E. Taylor, Thomas P. Caudell, and Kurt W. Larson

Abstract:

A recently-developed mathematical semantic theory explains the
relationship between
knowledge and its representation in connectionist systems. The semantic
theory
is based upon category theory, the mathematical theory of structure. A
product of its
explanatory capability is a set of principles to guide the design of
future neural architectures
and enhancements to existing designs. We claim that this mathematical
semantic
approach to network design is an effective basis for advancing the state
of the art. We
offer two experiments to support this claim. One of these involves
multispectral imaging
using data from a satellite camera.





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