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Entropy 2016, 18(6), 231; doi:10.3390/e18060231

Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights

MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering, School of Automation, Southeast University, Nanjing 210096, China
Department of Automation, Yancheng Institute of Technology, Yancheng 224051, China
Author to whom correspondence should be addressed.
Academic Editors: Badong Chen and Jose C. Principe
Received: 20 April 2016 / Revised: 16 June 2016 / Accepted: 16 June 2016 / Published: 21 June 2016
(This article belongs to the Special Issue Information Theoretic Learning)
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There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR. View Full-Text
Keywords: design time forecast; kernel-based regression; Kullback-Leibler divergence; heteroscedasticity design time forecast; kernel-based regression; Kullback-Leibler divergence; heteroscedasticity

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shang, Z.-G.; Yan, H.-S. Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights. Entropy 2016, 18, 231.

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