Nonlinear regression model generation using hyperparameter optimization

Strijov V., WEBER G. W.

COMPUTERS & MATHEMATICS WITH APPLICATIONS, cilt.60, sa.4, ss.981-988, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 4
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.camwa.2010.03.021
  • Dergi Adı: COMPUTERS & MATHEMATICS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.981-988
  • Anahtar Kelimeler: Regression, Coherent Bayesian inference, Hyperparameters, Model generation, Model selection
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.