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Proceedings Paper

A robust regularization algorithm for polynomial networks for machine learning
Author(s): Holger M. Jaenisch; James W. Handley
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Paper Abstract

We present an improvement to the fundamental Group Method of Data Handling (GMDH) Data Modeling algorithm that overcomes the parameter sensitivity to novel cases presented to derived networks. We achieve this result by regularization of the output and using a genetic weighting that selects intermediate models that do not exhibit divergence. The result is the derivation of multi-nested polynomial networks following the Kolmogorov-Gabor polynomial that are robust to mean estimators as well as novel exemplars for input. The full details of the algorithm are presented. We also introduce a new method for approximating GMDH in a single regression model using F, H, and G terms that automatically exports the answers as ordinary differential equations. The MathCAD 15 source code for all algorithms and results are provided.

Paper Details

Date Published: 19 May 2011
PDF: 21 pages
Proc. SPIE 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V, 80590A (19 May 2011); doi: 10.1117/12.884284
Show Author Affiliations
Holger M. Jaenisch, The Johns Hopkins Univ. (United States)
Licht Strahl Engineering, Inc. (United States)
James W. Handley, Licht Strahl Engineering, Inc. (United States)

Published in SPIE Proceedings Vol. 8059:
Evolutionary and Bio-Inspired Computation: Theory and Applications V
Misty Blowers; Teresa H. O'Donnell; Olga Lisvet Mendoza-Schrock, Editor(s)

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