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- W178263908 abstract "AbstractRegression models are well established tools in statistical analysis which date back early to the eighteenth century. Nonetheless, problems involved in their implementation and application in a wide number of fields are still the object of active research. Preliminary to the regression model estimation there is an identification step which has to be performed for selecting the variables of interest, detecting the relationships of interest among them, distinguishing dependent and independent variables. On the other hand, generalized regression models often have nonlinear and non convex log-likelihood, therefore maximum likelihood estimation requires optimization of complicated functions. In this chapter evolutionary computation methods are presented that have been developed to either support or surrogate analytic tools if the problem size and complexity limit their efficiency.KeywordsGenetic AlgorithmFitness FunctionIndependent Component AnalysisIndependent Component AnalysisBinary StringThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W178263908 created "2016-06-24" @default.
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- W178263908 date "2010-11-08" @default.
- W178263908 modified "2023-09-24" @default.
- W178263908 title "Evolving Regression Models" @default.
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- W178263908 doi "https://doi.org/10.1007/978-3-642-16218-3_3" @default.
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