An incremental learning method with relearning of recalled interfered patterns

This paper presents a new incremental learning method for neural networks. If a neural network is trained to memorize novel patterns only by their presentation, the network will forget some patterns that have been already learnt. This problem is caused by the fact that the learning of novel patterns usually interfere in the internal representation corresponding to the old training patterns. In the new method, the network recalls the patterns that the novel patterns possibly interfere in, and then learns both novel and recalled patterns. In the computer simulation, we demonstrate this system in the learning of alphabetic characters.