Results 31 to 40 of about 6,391,262 (321)

Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM [PDF]

Sensors, 2022
Muhammad Usama, In-Young Lee
exaly   +2 more sources

Symbolic Regression Methods for Reinforcement Learning

IEEE Access, 2021
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and ...
Jiri Kubalik, Erik Derner, Jan Zegklitz, Robert Babuska   +3 more
doaj   +1 more source

Symbolic Regression is NP-hard

Trans. Mach. Learn. Res., 2022
corrected citation Abbass 2002 -> Cramer ...
M. Virgolin (Marco), S. Pissis (Solon)
openaire   +4 more sources

End-to-end symbolic regression with transformers [PDF]

Neural Information Processing Systems, 2022
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the"skeleton"of the expression up to the choice of ...
Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, Franccois Charton   +3 more
semanticscholar   +1 more source

Call for Action: towards the next generation of symbolic regression benchmark [PDF]

GECCO Companion
Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria.
Guilherme Seidyo Imai Aldeia, Hengzhe Zhang, Geoffrey Bomarito, Miles D. Cranmer, A. Fonseca, Bogdan Burlacu, William G. La Cava, F. D. de França   +7 more
semanticscholar   +1 more source

Sequential Symbolic Regression with Genetic Programming [PDF]

, 2015
This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially ...
Oliveira, Luiz O.V.B., Albinati, Julio, Otero, Fernando E.B., Julio Albinati, Luiz Otávio V.B. Oliveira, Gisele L. Pappa, Pappa, Gisele L., Fernando E.B. Otero   +7 more
core   +1 more source

Likelihood-based Imprecise Regression [PDF]

, 2011
We introduce a new approach to regression with imprecisely observed data, combining likelihood inference with ideas from imprecise probability theory, and thereby taking different kinds of uncertainty into account.
Marco E. G. V. Cattaneo, Cattaneo, Marco E.G.V., Wiencierz, Andrea, Cattaneo, Marco E. G. V., Andrea Wiencierz   +4 more
core   +1 more source

Robust regression with imprecise data [PDF]

, 2011
We consider the problem of regression analysis with imprecise data. By imprecise data we mean imprecise observations of precise quantities in the form of sets of values.
Wiencierz, Andrea, Cattaneo, Marco E. G. V.   +1 more
core   +1 more source

SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond [PDF]

Industrial & Engineering Chemistry Research
Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data.
Madhav R. Muthyala, Farshud Sorourifar, Peng You, Joel A. Paulson   +3 more
semanticscholar   +1 more source

The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems [PDF]

, 2015
This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process ...
Albinati, Julio, Otero, Fernando E.B., Julio Albinati, Luiz Otávio V. B. Oliveira, Gisele L. Pappa, Pappa, Gisele L., Oliveira, Luiz Otávio V.B., Fernando E. B. Otero   +7 more
core   +1 more source