Different approaches to fitting and extrapolating the learning curve

Bachelor Thesis (2022)
Author(s)

D. KIM (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Tom Julian Viering – Mentor (TU Delft - Computer Science & Engineering-Teaching Team)

M. Loog – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Georgios Smaragdakis – Graduation committee member (TU Delft - Cyber Security)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 DONGHWI KIM
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 DONGHWI KIM
Graduation Date
23-06-2022
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the desired performance. It can be beneficial when gathering data is complex, or computation resource is limited. One of the essential processes of learning curve extrapolation is curve fitting. This research first analyses the behaviour of existing curve fitting methods such as Newton, Levenberg-Marquardt and Evolutionary algorithms when fitting different function models on learning curves. Furthermore, it also illustrates a few techniques to improve the learning curve fitting and extrapolation procedure.

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