76 Predicting the Learning Performance of Artificial Intelligent Systems Using Non-Homogeneous Poisson Process Models
Download citation file:
Artificial intelligent systems can learn to adapt to environmental changes to find a better solution. Improving system performance has been a great interest of study with new objective functions and parameters being constantly applied. However, approaches for performance evaluation are often by real observation without offering a prediction capability to support decision making at run time. Prediction helps foresee the future, so that a good run can continue while a poor one can be replaced. It also assists in evaluating the efficacy of different algorithms, especially when their learning capabilities vary over time. In this paper, a statistical approach is...