Open AccessProceedings Article
Maximum Likelihood Competitive Learning
Steven J. Nowlan
- 01 Jan 1989
- Vol. 2, pp 574-582
TL;DR: This work proposes to view competitive adaptation as attempting to fit a blend of simple probability generators to a set of data-points, and investigates one application of the soft competitive model, placement of radial basis function centers for function interpolation, and shows that the soft model can give better performance with little additional computational cost.
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Abstract: One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor, the winner, adapts for any given case. I propose to view competitive adaptation as attempting to fit a blend of simple probability generators (such as gaussians) to a set of data-points. The maximum likelihood fit of a model of this type suggests a "softer" form of competition, in which all competitors adapt in proportion to the relative probability that the input came from each competitor. I investigate one application of the soft competitive model, placement of radial basis function centers for function interpolation, and show that the soft model can give better performance with little additional computational cost.
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References
Self-organized formation of topologically correct feature maps
TL;DR: In this paper, the authors describe a self-organizing system in which the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events.
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•Journal Article
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
David S. Broomhead,David Lowe +1 more
TL;DR: The relationship between 'learning' in adaptive layered networks and the fitting of data with high dimensional surfaces is discussed, leading naturally to a picture of 'generalization in terms of interpolation between known data points and suggests a rational approach to the theory of such networks.
Networks for approximation and learning
Tomaso Poggio,Federico Girosi +1 more
- 01 Sep 1990
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