Online Machine Learning Techniques for Predicting Operator Performance

TL;DR

This thesis investigates online machine learning algorithms for operator performance prediction, focusing on their theoretical suitability, efficient implementation, and rigorous evaluation for a specific function approximation task.

Contribution

It provides a comprehensive assessment of online learning algorithms' applicability and efficiency for operator performance prediction, including implementation and evaluation methods.

Findings

Algorithms are effective for the specific function approximation problem.

Efficient implementation techniques improve computational performance.

Rigorous testing validates the algorithms' suitability for real-world application.

Abstract

This thesis explores a number of online machine learning algorithms. From a theoret- ical perspective, it assesses their employability for a particular function approximation problem where the analytical models fall short. Furthermore, it discusses the applica- tion of theoretically suitable learning algorithms to the function approximation problem at hand through an efficient implementation that exploits various computational and mathematical shortcuts. Finally, this thesis work evaluates the implemented learning algorithms according to various evaluation criteria through rigorous testing.