Online Machine Learning Techniques for Predicting Operator Performance
It addresses a specific function approximation problem, but appears incremental as it focuses on applying existing online learning techniques.
This thesis applied online machine learning algorithms to a function approximation problem where analytical models were insufficient, evaluating them through rigorous testing to assess their performance.
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.