Geometrics of knowledge.
It is argued that knowledge representations formalized through pattern theoretic structures are geometric in nature in the following sense. The configurations and resulting patterns appearing in such representations exhibit invariances with respect to the similarity groups and are characterized topologically through their connection types. Starting with a special pattern from microbiology, it is shown how the basic pattern theoretic concepts are introduced in general and what their function is in representing knowledge. Variance/invariance of the patterns is discussed in geometric language. The measures on the configuration spaces are implemented by difference/differential equations which are used as a basis for computer algorithms.
Grenander
U
U
Brown University, Providence, RI 02912, USA.
D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008928:Mitochondria; D008962:Models, Theoretical; D051381:Rats