Geometric Learning Algorithms
Title | Geometric Learning Algorithms |
Publication Type | Technical Report |
Year of Publication | 1989 |
Authors | Omohundro, S. |
Other Numbers | 540 |
Keywords | computational geometry, emergent computation, learning algorithms, neural networks, robotics |
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, 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. |
URL | http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1989/tr-89-041.pdf |
Bibliographic Notes | ICSI Technical Report TR-89-041 |
Abbreviated Authors | S. M. Omohundro |
ICSI Publication Type | Technical Report |