Characterising the Double Descent of Symbolic Regression

About this item

Title

Characterising the Double Descent of Symbolic Regression

Content partner

University of Otago

Collection

Otago University Research Archive

Description

Recent work has argued that many machine learning techniques exhibit a 'double descent' in model risk, where increasing model complexity beyond an interpolation zone can overcome the bias-variance tradeoff to produce large, over-parameterised models that generalise well to unseen data. While the double descent characteristic has been identified in many learning methods, it has not been explored within symbolic regression research. This paper presents an initial exploration into the presence o...

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Format

Research paper

Research format

Scholarly text / Conference paper

Thesis level

Conference Proceedings

Date created

2024-07-14

Creator

Dick, Grant / Owen, Caitlin

URL

https://hdl.handle.net/10523/41932

Related subjects

Computing methodologies -- Machine learning -- Learning paradigms -- Supervised learning -- Supervised learning by regression / Computing methodologies -- Machine learning -- Machine learning approaches -- Bio-inspired approaches -- Genetic programming

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