Publication Details
Title: Geometric Learning Algorithms
Author: S. M. Omohundro
Group: ICSI Technical Reports
Date: June 1989
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1989/tr-89-041.pdf
Overview:
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. Keywords: learning algorithms, neural networks, computational geometry, emergent computation, robotics.
Bibliographic Information:
ICSI Technical Report TR-89-041
Bibliographic Reference:
S. M. Omohundro. Geometric Learning Algorithms. ICSI Technical Report TR-89-041, June 1989
Author: S. M. Omohundro
Group: ICSI Technical Reports
Date: June 1989
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1989/tr-89-041.pdf
Overview:
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. Keywords: learning algorithms, neural networks, computational geometry, emergent computation, robotics.
Bibliographic Information:
ICSI Technical Report TR-89-041
Bibliographic Reference:
S. M. Omohundro. Geometric Learning Algorithms. ICSI Technical Report TR-89-041, June 1989