Geometric Learning Algorithms

TitleGeometric Learning Algorithms
Publication TypeTechnical Report
Year of Publication1989
AuthorsOmohundro, S.
Other Numbers540
Keywordscomputational geometry, emergent computation, learning algorithms, neural networks, robotics

Emergent computation in the form of geometric learning is central to the development of motor and perceptual systems in biological organisms and promises to have a similar impact on emerging technologies including robotics, vision, speech, and graphics. This paper examines some of the trade-offs involved in different implementation strategies, focusing on the tasks of learning discrete classifications and smooth nonlinear mappings. The trade-offs between local and global representations are discussed, a spectrum of distributed network implementations are examined, and an important source of computational inefficiency is identified. Efficient algorithms based on k-d trees and the Delaunay triangulation are presented and the relevance to biological networks is discussed. Finally, extensions of both the tasks and the implementations are given.

Bibliographic Notes

ICSI Technical Report TR-89-041

Abbreviated Authors

S. M. Omohundro

ICSI Publication Type

Technical Report