Results 11 to 20 of about 150,984 (329)
AI Feynman: a Physics-Inspired Method for Symbolic Regression [PDF]
Sci Adv, 2020 A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.
Tegmark, Max, Udrescu, Silviu-Marian
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Regression Models for Symbolic Interval-Valued Variables [PDF]
Entropy, 2021 This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De ...
Jose Emmanuel Chacón +1 more
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Knowledge-Guided Symbolic Regression for Interpretable Camera Calibration [PDF]
Journal of ImagingCalibrating cameras accurately requires the identification of projection and distortion models that effectively account for lens-specific deviations. Conventional formulations, like the pinhole model or radial–tangential corrections, often struggle to ...
Rui Pimentel de Figueiredo
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Application of the symbolic regression program AI-Feynman to psychology [PDF]
Frontiers in Artificial Intelligence, 2023 The discovery of hidden laws in data is the core challenge in many fields, from the natural sciences to the social sciences. However, this task has historically relied on human intuition and experience in many areas, including psychology.
Masato Miyazaki +5 more
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Symbolic regression of generative network models [PDF]
Scientific Reports, 2014 Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood.
Menezes, Telmo, Roth, Camille
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Glyph: Symbolic Regression Tools
Journal of Open Research Software, 2019 We present Glyph – a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments.
Markus Quade, Julien Gout, Markus Abel
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The Application of Symbolic Regression on Identifying Implied Volatility Surface [PDF]
Mathematics, 2023 One important parameter in the Black–Scholes option pricing model is the implied volatility. Implied volatility surface (IVS) is an important concept in finance that describes the variation of implied volatility across option strike price and time to ...
Jiayi Luo, Cindy Long Yu
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Learning interpretable network dynamics via universal neural symbolic regression [PDF]
Nature CommunicationsDiscovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the hidden patterns and mechanisms of the formation and evolution of complex phenomena in various fields and ...
Jiao Hu, Jiaxu Cui, Bo Yang
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Improving eye-tracking calibration accuracy using symbolic regression. [PDF]
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 +2 more
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The Journal of SupercomputingAbstract This article introduces a new symbolic regression algorithm based on the SPINEX (similarity-based predictions with explainable neighbors exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics.
M.Z. Naser, Ahmad Z. Naser
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