Nonlinear regression model generation using hyperparameter optimization

Date

2010-08-01

Author

Strijov, Vadim
Weber, Gerhard Wilhelm

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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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Citation Formats

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.