Abstract
This note describes a simple technique, the Gamma (or Near Neighbour) test, which in many cases can be used to considerably simplify the design process of constructing a smooth data model such as a neural network. The Gamma test is a data analysis routine, that (in an optimal implementation) runs in time O(MlogM)as M→∞,where Mis the number of sample data points, and which aims to estimate the best Mean Squared Error (MSError) that can be achieved by any continuous or smooth (bounded first partial derivatives) data model constructed using the data.
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References
Friedman JH, Bentley JL, Finkel RA.An algorithm for finding best matches in logarithmic expected time. ACM Trans Math Softw 1977; 3(3): 200–226.
Končar N.Optimisation methodologies for direct inverse neurocontrol. PhD thesis, Department of Computing, Imperial College, UK
Otani, M, Jones AJ.Guiding chaotic orbits. Research Report, Department of Computer Science, University of Wales, Cardiff, UK
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Stefánsson, A., Končar, N. & Jones, A.J. A note on the Gamma test. Neural Comput & Applic 5, 131–133 (1997). https://doi.org/10.1007/BF01413858
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DOI: https://doi.org/10.1007/BF01413858