Results 41 to 50 of about 150,984 (329)
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 ...
Pitzer, Erik, Kronberger, Gabriel
openaire +2 more sources
Priors for symbolic regression
Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023 8+2 pages, 2 figures.
Bartlett, D, Desmond, H, Ferreira, P
openaire +3 more sources
Selecting Informative Data Samples for Model Learning Through Symbolic Regression
IEEE Access, 2021 Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy
Erik Derner +2 more
doaj +1 more source
The ubiquitin ligase RNF115 is required for the clearance of damaged lysosomes
FEBS Letters, EarlyView.Upon lysosomal rupture, an E3 ubiquitin ligase RNF115 translocates from the cytosol to the damaged lysosomal membrane. Moreover, RNF115 depletion impairs the clearance of damaged lysosomes, identifying it as a key regulator of lysosomal quality control.
Sae Nakanaga +3 more
wiley +1 more source
$\mathcal{CP}$-analyses with symbolic regression
SciPost PhysicsSearching 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
Temporal Feature Selection with Symbolic Regression [PDF]
, 2017 Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the ...
Fusting, Christopher Winter
core +1 more source
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach
, 2018 The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent matter in the research community. This paper takes a new step in this direction, proposing a different approach to measure the quality of the symbolic ...
Martins, Joao Francisco Barreto da Silva +3 more
core +1 more source
Recruiting nurses through social media : effects on employer brand and attractiveness [PDF]
, 2017 Aim: To investigate whether and how nurses' exposure to a hospital's profile on social media affects their perceptions of the hospital's brand and attractiveness as an employer.
Carpentier, Marieke +5 more
core +2 more sources
, 2019 Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions.
Jin, Ying +4 more
openaire +2 more sources
LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro
Molecular Oncology, EarlyView.Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix +13 more
wiley +1 more source