Symbolic regression

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위키데이터

• ID : Q831366

말뭉치

• On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results.
• Discovering unmodeled components in astrodynamics with symbolic regression.
• The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data.
• The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.
• The process of generating a computer program to fit numerical data is called symbolic regression.
• The authors showcase the potential of symbolic regression as an analytic method for use in materials research.
• Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science.
• In this prospective paper, we focus on an alternative to machine-learning models: symbolic regression.
• I think symbolic regression is a great tool to be aware of.
• By not requiring a specific model to be specified, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge.
• Symbolic regression is one of the best known problems in GP (see Reference).
• As any evolutionary program, symbolic regression needs (at least) two object types : an individual containing the genotype and a fitness.
• "Predicting friction system performance with symbolic regression and genetic programming with factor variables".
• “Prediction of Stress-Strain Curves for Aluminium Alloys using Symbolic Regression”.
• Symbolic regression is a data-based modelling method where the goal is to find a formula that describes given data.
• However, in symbolic regression one does not merely fit parameters to a fixed model structure.
• One of the more interesting ones is symbolic regression, which is used in Eureqa models within DataRobot.
• Perhaps the most common technique used in symbolic regression is genetic programming2 (GP).
• Let’s use the “Auto MPG” data from UCI: http://archive.ics.uci.edu/ml/datasets/Auto+MPG to understand symbolic regression.
• One of the key decisions in symbolic regression is how to represent the programs we are creating3.
• This was an example of symbolic regression: discovering a symbolic expression that accurately matches a given dataset.
• However, as we will see below, this does not prevent us from discovering and exploiting these properties to facilitate symbolic regression.
• Here is my code to compute Symbolic Regression and plot the function.
• Symbolic Regression also tries to fit observed experimental data.
• There are different ways to represent the solutions in Symbolic Regression.
• In Symbolic Regression, many initially random symbolic equations compete to model experimental data in the most promising way.
• Symbolic Regression is used to solve this task.
• It is known that symbolic regression is a widely used method for mathematical function approximation.
• The flowchart of symbolic regression based on genetic programming (see more details of this flowchart and SR in Supplementary Information).
• Symbolic regression (SR) is a powerful method for building predictive models from data without assuming any model structure.
• In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit.
• This paper extends recent work on the importance of standardisation of features when conducting symbolic regression.
• Other symbolic regression libraries Due to its popularity, symbolic regression is implemented by most genetic programming libraries.
• Right: gp-based symbolic regression finds different candidate control laws.

메타데이터

위키데이터

• ID : Q831366