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- Q18171762 subject Q6425293.
- Q18171762 subject Q8482279.
- Q18171762 subject Q8646998.
- Q18171762 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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- Q18171762 wikiPageExternalLink galesia97surveyofGP.pdf.
- Q18171762 wikiPageExternalLink index.html.
- Q18171762 wikiPageExternalLink Science09_Schmidt.pdf.
- Q18171762 wikiPageExternalLink dev.heuristiclab.com.
- Q18171762 wikiPageExternalLink www.symbolicregression.com.
- Q18171762 wikiPageExternalLink gptips4matlab.
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- Q18171762 wikiPageWikiLink Q6425293.
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- Q18171762 wikiPageWikiLink Q777407.
- Q18171762 wikiPageWikiLink Q8482279.
- Q18171762 wikiPageWikiLink Q8646998.
- Q18171762 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.".
- Q18171762 label "Symbolic regression".