A point-pass-based action prediction method
Jinan Xu, Takayuki Itoh, Kenji Araki, Koji Tochinai · 2005
The paper describes a basic idea on how to realize an intelligent learning room system. Such a system needs to have a dynamic adaptive capability for each user. We have proposed a method to predict user action using inductive learning with N-gram. The system based on our proposed method is able to acquire rules automatically from data pairs through inductive learning. As unified with N-gram, the system demonstrates a high predictive accuracy. However, the acquired rules express the user's habits and preferences. Consequently, it is possible that the system adapts dynamically to each user. The user needs to proof-read the errors in the prediction results. Therefore, the prediction ability improves. As a result, the number of errors decreases. This paper unifies N-gram and inductive learning to develop the point-pass-based prediction system. The system was found to have good accuracy of which the highest prediction accuracy was about 89.3%. The system was proved to have high dynamic adaptive ability.