Symbolic regression
노트
위키데이터
- ID : Q831366
말뭉치
- On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results.[1]
- Discovering unmodeled components in astrodynamics with symbolic regression.[2]
- The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data.[2]
- The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.[2]
- The process of generating a computer program to fit numerical data is called symbolic regression.[3]
- The authors showcase the potential of symbolic regression as an analytic method for use in materials research.[4]
- Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science.[4]
- In this prospective paper, we focus on an alternative to machine-learning models: symbolic regression.[5]
- I think symbolic regression is a great tool to be aware of.[5]
- By not requiring a specific model to be specified, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge.[6]
- Symbolic regression is one of the best known problems in GP (see Reference).[7]
- As any evolutionary program, symbolic regression needs (at least) two object types : an individual containing the genotype and a fitness.[7]
- "Predicting friction system performance with symbolic regression and genetic programming with factor variables".[8]
- “Prediction of Stress-Strain Curves for Aluminium Alloys using Symbolic Regression”.[8]
- Symbolic regression is a data-based modelling method where the goal is to find a formula that describes given data.[8]
- However, in symbolic regression one does not merely fit parameters to a fixed model structure.[8]
- One of the more interesting ones is symbolic regression, which is used in Eureqa models within DataRobot.[9]
- Perhaps the most common technique used in symbolic regression is genetic programming2 (GP).[9]
- Let’s use the “Auto MPG” data from UCI: http://archive.ics.uci.edu/ml/datasets/Auto+MPG to understand symbolic regression.[9]
- One of the key decisions in symbolic regression is how to represent the programs we are creating3.[9]
- This was an example of symbolic regression: discovering a symbolic expression that accurately matches a given dataset.[10]
- However, as we will see below, this does not prevent us from discovering and exploiting these properties to facilitate symbolic regression.[10]
- Here is my code to compute Symbolic Regression and plot the function.[11]
- Symbolic Regression also tries to fit observed experimental data.[12]
- There are different ways to represent the solutions in Symbolic Regression.[12]
- In Symbolic Regression, many initially random symbolic equations compete to model experimental data in the most promising way.[12]
- Symbolic Regression is used to solve this task.[12]
- It is known that symbolic regression is a widely used method for mathematical function approximation.[13]
- The flowchart of symbolic regression based on genetic programming (see more details of this flowchart and SR in Supplementary Information).[14]
- Symbolic regression (SR) is a powerful method for building predictive models from data without assuming any model structure.[15]
- In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit.[16]
- This paper extends recent work on the importance of standardisation of features when conducting symbolic regression.[16]
- Other symbolic regression libraries Due to its popularity, symbolic regression is implemented by most genetic programming libraries.[17]
- Right: gp-based symbolic regression finds different candidate control laws.[18]
소스
- ↑ RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming
- ↑ 2.0 2.1 2.2 Discovering unmodeled components in astrodynamics with symbolic regression
- ↑ Symbolic Regression Genetic Programming Example
- ↑ 4.0 4.1 Symbolic regression in materials science
- ↑ 5.0 5.1 gplearn Symbolic Regression
- ↑ Symbolic regression
- ↑ 7.0 7.1 Symbolic Regression Problem: Introduction to GP — DEAP 1.3.1 documentation
- ↑ 8.0 8.1 8.2 8.3 Josef Ressel Centre for Symbolic Regression
- ↑ 9.0 9.1 9.2 9.3 Symbolic Regression from Scratch with Python
- ↑ 10.0 10.1 AI Feynman: A physics-inspired method for symbolic regression
- ↑ How to get the function result from Symbolic Regression with R
- ↑ 12.0 12.1 12.2 12.3 Learn More about Your Data: A Symbolic Regression Knowledge Representation Framework
- ↑ Symbolic Regression Problems by Genetic Programming with Multi-branches
- ↑ Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
- ↑ Benchmarking state-of-the-art symbolic regression algorithms
- ↑ 16.0 16.1 Feature standardisation and coefficient optimisation for effective symbolic regression
- ↑ Glyph: Symbolic Regression Tools
- ↑ (PDF) Glyph: Symbolic Regression Tools
메타데이터
위키데이터
- ID : Q831366