- S. Omohundro, R. Paul, M. Shamos, D. Walters, J. Cowan, J. Crutchfield, Bruce S. McNamara, L. Devroye, S. Harnad, J. Friedman, Jon Louis Bently, R. Finkel, D. R. Hougen. 1990. Geometric Learning Algorithms Geometric Learning Algorithms. Abstract: 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, focussing 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.
