Results 1 to 10 of about 13,019 (261)

Hybrid Symbolic Regression with the Bison Seeker Algorithm

Mendel, 2019
This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression.
Jan Merta
doaj   +1 more source

Deep Generative Symbolic Regression

CoRR, 2023
In the proceedings of the Eleventh International Conference on Learning Representations (ICLR 2023).
Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar   +2 more
openaire   +3 more sources

Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms, 2021
Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature.
Estrella Lucena-Sánchez, Guido Sciavicco, Ionel Eduard Stan   +2 more
doaj   +1 more source

Exhaustive Symbolic Regression

IEEE Transactions on Evolutionary Computation
15 pages, 7 figures, 2 tables.
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira   +2 more
openaire   +4 more sources

Symbolic Regression Algorithms with Built-in Linear Regression

CoRR, 2017
Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude ...
Jan Zegklitz, Petr Posík
openaire   +2 more sources

Improving eye-tracking calibration accuracy using symbolic regression.

PLoS ONE, 2019
Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations.
Almoctar Hassoumi, Vsevolod Peysakhovich, Christophe Hurter   +2 more
doaj   +1 more source

Control Synthesis as Machine Learning Control by Symbolic Regression Methods

Applied Sciences, 2021
The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods.
Elizaveta Shmalko, Askhat Diveev
doaj   +1 more source

Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

Energies, 2022
In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed.
Krzysztof Drachal
doaj   +1 more source

$\mathcal{CP}$-analyses with symbolic regression

SciPost Physics
Searching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially ...
Henning Bahl, Elina Fuchs, Marco Menen, Tilman Plehn
doaj   +1 more source

Multiview Symbolic Regression

Proceedings of the Genetic and Evolutionary Computation Conference
Published in GECCO-2024.
Etienne Russeil, Fabrício Olivetti de França, Konstantin L. Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler   +8 more
openaire   +3 more sources