Symbolic and numerical regression: experiments and applications

This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the C...

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Vydáno v:Information sciences Ročník 150; číslo 1; s. 95 - 117
Hlavní autoři: Davidson, J.W., Savic, D.A., Walters, G.A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.03.2003
Témata:
Genetic programming
Least squares
Rule-based programming
Stepwise regression
Symbolic regression
Symbolic regression
Rule-based programming
Stepwise regression
Genetic programming
Least squares
ISSN:0020-0255, 1872-6291
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Abstract This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook–White equation and rainfall-runoff modelling. The three example problems illustrate the advantages of the new method.
AbstractList This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook–White equation and rainfall-runoff modelling. The three example problems illustrate the advantages of the new method.
Author Savic, D.A.
Walters, G.A.
Davidson, J.W.
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Keywords Symbolic regression
Rule-based programming
Stepwise regression
Genetic programming
Least squares
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SubjectTerms Genetic programming
Least squares
Rule-based programming
Stepwise regression
Symbolic regression
Title Symbolic and numerical regression: experiments and applications
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