Nonlinear regression model generation using hyperparameter optimization

Strijov V., WEBER G. W.

COMPUTERS & MATHEMATICS WITH APPLICATIONS, vol.60, no.4, pp.981-988, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60 Issue: 4
  • Publication Date: 2010
  • Doi Number: 10.1016/j.camwa.2010.03.021
  • Journal Name: COMPUTERS & MATHEMATICS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.981-988
  • Keywords: Regression, Coherent Bayesian inference, Hyperparameters, Model generation, Model selection
  • Middle East Technical University Affiliated: Yes

Abstract

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. (C) 2010 Elsevier Ltd. All rights reserved.