Additional learning and forgetting by potential method for pattern classification

In analogy to growth of human beings, machine learning should make additional learning if some new knowledge is obtained. This situation occurs very often in practical problems in which the environment changes over time, e.g., in financial investment problems. The well known backpropagation method in artificial neural networks is not effective for such additional learning. On the other hand, the potential method can make additional learning very easily. In this paper, the effectiveness of the potential method is shown from a viewpoint of additional learning. Furthermore, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. Examples in stock portfolio problems show that the performance of potential method increases more with additional learning and forgetting