Results 151 to 160 of about 6,391,262 (321)
Fast Symbolic Regression Benchmarking
Symbolic regression (SR) uncovers mathematical models from data. Several benchmarks have been proposed to compare the performance of SR algorithms. However, existing ground-truth rediscovery benchmarks overemphasize the recovery of "the one" expression form or rely solely on computer algebra systems (such as SymPy) to assess success.
openaire +4 more sources
Parallel Implementation of Symbolic Regression [PDF]
Svět kolem nás je plný neprozkoumaných dat. Tato diplomová práce se zaměřuje na jejich prozkoumání pomocí symbolické regrese, která je založena na hledání vzorečku nejlépe popisujícího hodnoty funkce použité pro vytvoření datasetu.
Malíček Tomáš
core
Advanced Science, EarlyView.A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai +3 more
wiley +1 more source
CoRRThis 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, Ahmed Naser
openaire +3 more sources
Advanced Science, EarlyView.An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen +7 more
wiley +1 more source
Advanced Science, EarlyView.A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley +1 more source
Predictive Models of Adult Distance Learners' Academic Performance: Comparative Analysis of Two Regression-based Models of Path Analysis [PDF]
, 2011 Using two regression-based models, this study looked into factors that explain academic performance of adult distance learners. The efficiency and applicability of linear regression and logit regression procedures as statistical models for path analysis ...
Quimbo, Maria Ana T.
core
Slc44a2 Deficiency Unveils an IFN‐I–Dependent Feedback Control of pDC Egress
Advanced Science, EarlyView.Working model of SLC44A2‐mediated maintenance of pDC homeostasis. This model illustrates two central mechanisms by which SLC44A2 regulates pDC homeostasis: (1) SLC44A2 limits IFN‐I production by exporting amino acids (T, N, Q), thereby preventing spontaneous pDC activation.
Ruiqun Chen +11 more
wiley +1 more source
Advanced Science, EarlyView.Caspofungin heteroresistance is prevalent in clinical Candida glabrata isolates and depends on calcineurin‐mediated stress adaptation. This transient phenotype serves as a reservoir for resistance evolution, enabling the emergence of stable resistant descendants under prolonged drug pressure.
Yanyu Su +7 more
wiley +1 more source
Exploring Symbolic Regression and Genetic Algorithms for Astronomical Object Classification
The Open Journal of AstrophysicsThis study explores the use of symbolic regression (SR) combined with genetic algorithms (GA) to classify astronomical objects. Using the SDSS17 dataset from Kaggle, which includes 100,000 observations of stars, galaxies, and quasars, we applied SR to 10%
Fabio Ricardo Llorella +1 more
doaj +1 more source