Self-Organizing Neural Networks using Discontinuous Teacher Data for Incremental Category Learning

In this paper, we develop a neural model that forms categories of inputs for some practical applications such as pattern recognition, learning, image processing, and trend analysis. The developed model is based on natural mechanisms of biological behavior instead of artificial one such as clustering algorithms. The essential point of the model is to regard the teacher information as a first priority for an accurate learning. Then, the model can carry the accurate classification of complex and imbalanced categories by using discontinuous teacher data under an incremental learning environment. Simulation results demonstrate the usefulness and the weakness of the model on practical category formation tasks.