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Nonlinear regression model generation using hyperparameter optimization

Strijov, Vadim
Weber, Gerhard Wilhelm
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.