Results 21 to 30 of about 12,301 (257)
Symbolic regression in materials science [PDF]
MRS Communications, 2019 We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques.
Wang, Yiqun +2 more
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Neural Symbolic Regression that Scales
, 2021 Accepted at the 38th International Conference on Machine Learning (ICML ...
Biggio, Luca +4 more
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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
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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
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Interaction–Transformation Evolutionary Algorithm for Symbolic Regression [PDF]
Evolutionary Computation, 2021 AbstractInteraction–Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree.
de Franca, Fabricio Olivetti +1 more
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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
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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 +2 more
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Exhaustive Symbolic Regression
IEEE Transactions on Evolutionary Computation15 pages, 7 figures, 2 tables.
Deaglan J. Bartlett +2 more
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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
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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
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