Results 21 to 30 of about 2,854,398 (286)

Neural Symbolic Regression that Scales

CoRR, 2021
Accepted at the 38th International Conference on Machine Learning (ICML ...
Biggio, Luca, Bendinelli, Tommaso, Neitz, Alexander, Lucchi, Aurelien, Parascandolo, Giambattista   +4 more
openaire   +5 more sources

Controllable Neural Symbolic Regression

CoRR, 2023
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a
Bendinelli, Tommaso, Biggio, Luca, Kamienny, Pierre-Alexandre   +2 more
openaire   +4 more sources

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

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

Online Symbolic Regression with Informative Query

, 2023
Symbolic regression, the task of extracting mathematical expressions from the observed data, plays a crucial role in scientific discovery. Despite the promising performance of existing methods, most of them conduct symbolic regression in an offline ...
Jin, Pengwei, Chen, Yunji, Hu, Xing, Du, Zidong, Zhang, Rui, Huang, Di, Nan, Ziyuan, Guo, Qi   +7 more
core   +2 more sources

Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-Valued Optimization Problem [PDF]

, 2015
The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real ...
Erik Pitzer, Gabriel Kronberger
openaire   +3 more sources

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

Symbolic regression for the interpretation of quantitative structure-property relationships

Artificial Intelligence in the Life Sciences, 2022
The interpretation of quantitative structure–activity or structure–property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high ...
Katsushi Takaki, Tomoyuki Miyao
doaj   +1 more source

The Lookup Table Regression Model for Histogram-Valued Symbolic Data

Stats, 2022
This paper presents the Lookup Table Regression Model (LTRM) for histogram-valued symbolic data. We first transform the given symbolic data to a numerical data table by the quantile method.
Manabu Ichino
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   +6 more sources