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Nonlinear regression model generation using hyperparameter optimization
Date
2010-08-01
Author
Strijov, Vadim
Weber, Gerhard Wilhelm
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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An algorithm of the inductive model generation and model selection is proposed to solve the problem of automatic construction of regression models. A regression model is an admissible superposition of smooth functions given by experts. Coherent Bayesian inference is used to estimate model parameters. It introduces hyperparameters which describe the distribution function of the model parameters. The hyperparameters control the model generation process.
Subject Keywords
Modelling and Simulation
,
Computational Theory and Mathematics
,
Computational Mathematics
URI
https://hdl.handle.net/11511/56351
Journal
COMPUTERS & MATHEMATICS WITH APPLICATIONS
DOI
https://doi.org/10.1016/j.camwa.2010.03.021
Collections
Graduate School of Applied Mathematics, Article
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BibTeX
V. Strijov and G. W. Weber, “Nonlinear regression model generation using hyperparameter optimization,”
COMPUTERS & MATHEMATICS WITH APPLICATIONS
, pp. 981–988, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56351.