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- Symbolic_regression abstract "Symbolic regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point to the algorithm. Instead, initial expressions are formed by randomly combining mathematical building blocks such as mathematical operators, analytic functions, constants, and state variables. (Usually, a subset of these primitives will be specified by the person operating it, but that's not a requirement of the technique.) New equations are then formed by recombining previous equations, using genetic programming.By not requiring a specific model to be specified, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge. It attempts to uncover the intrinsic relationships of the dataset, by letting the patterns in the data itself reveal the appropriate models, rather than imposing a model structure that is deemed mathematically tractable from a human perspective. The fitness function that drives the evolution of the models takes into account not only error metrics (to ensure the models accurately predict the data), but also special complexity measures, thus ensuring that the resulting models reveal the data's underlying structure in a way that's understandable from a human perspective. This facilitates reasoning and favors the odds of getting insights about the data-generating system.".
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- Symbolic_regression wikiPageExternalLink galesia97surveyofGP.pdf.
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- Symbolic_regression wikiPageWikiLink American_Association_for_the_Advancement_of_Science.
- Symbolic_regression wikiPageWikiLink Analytic_function.
- Symbolic_regression wikiPageWikiLink Category:Computer_algebra.
- Symbolic_regression wikiPageWikiLink Category:Genetic_programming.
- Symbolic_regression wikiPageWikiLink Category:Regression_analysis.
- Symbolic_regression wikiPageWikiLink Closed-form_expression.
- Symbolic_regression wikiPageWikiLink Constant_(mathematics).
- Symbolic_regression wikiPageWikiLink DataMelt.
- Symbolic_regression wikiPageWikiLink Eureqa.
- Symbolic_regression wikiPageWikiLink Evolutionary_algorithm.
- Symbolic_regression wikiPageWikiLink Fitness_function.
- Symbolic_regression wikiPageWikiLink Genetic_programming.
- Symbolic_regression wikiPageWikiLink Institution_of_Electrical_Engineers.
- Symbolic_regression wikiPageWikiLink John_Koza.
- Symbolic_regression wikiPageWikiLink Kolmogorov_complexity.
- Symbolic_regression wikiPageWikiLink Mathematical_optimization.
- Symbolic_regression wikiPageWikiLink Operation_(mathematics).
- Symbolic_regression wikiPageWikiLink Regression_analysis.
- Symbolic_regression wikiPageWikiLink Residual_(numerical_analysis).
- Symbolic_regression wikiPageWikiLink Science_(journal).
- Symbolic_regression wikiPageWikiLink State_variable.
- Symbolic_regression wikiPageWikiLinkText "Symbolic regression".
- Symbolic_regression wikiPageWikiLinkText "symbolic regression".
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- Symbolic_regression subject Category:Computer_algebra.
- Symbolic_regression subject Category:Genetic_programming.
- Symbolic_regression subject Category:Regression_analysis.
- Symbolic_regression hypernym Analysis.
- Symbolic_regression type Work.
- Symbolic_regression comment "Symbolic regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point to the algorithm. Instead, initial expressions are formed by randomly combining mathematical building blocks such as mathematical operators, analytic functions, constants, and state variables.".
- Symbolic_regression label "Symbolic regression".
- Symbolic_regression sameAs 関数同定問題.
- Symbolic_regression sameAs m.010qpcpg.
- Symbolic_regression sameAs Q18171762.
- Symbolic_regression sameAs Q18171762.
- Symbolic_regression wasDerivedFrom Symbolic_regression?oldid=676548657.
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