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Active learning improves performance on symbolic regression..:
, In:
Proceedings of the Genetic and Evolutionary Computation Conference Companion
,
Haut, Nathan
;
Banzhaf, Wolfgang
;
Punch, Bill
- p. 550-553 , 2022
Link:
https://dl.acm.org/doi/10.1145/3520304.3528941
RT T1 <cTitle>Proceedings of the Genetic and Evolutionary Computation Conference Companion</cTitle>: T1 <Title>Active learning improves performance on symbolic regression tasks in StackGP</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3528941&Exemplar=1&LAN=DE A1 Haut, Nathan A1 Banzhaf, Wolfgang A1 Punch, Bill PB ACM YR 2022 K1 active learning K1 genetic programming K1 symbolic regression K1 Computing methodologies K1 Symbolic and algebraic manipulation K1 Representation of mathematical objects K1 Representation of mathematical functions K1 Machine learning K1 Learning settings K1 Active learning settings K1 Machine learning approaches K1 Bio-inspired approaches K1 Genetic programming K1 Learning paradigms K1 Supervised learning K1 Supervised learning by regression SP 550 OP 553 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3520304.3528941 DO https://dl.acm.org/doi/10.1145/3520304.3528941 SF ELIB - SuUB Bremen
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