Adaptive Pattern Classification

Abstract

Up until the recent past, the power of multi layer feed forward artificial neural networks has been untapped mainly due to the lack of algorithms to train them. With the emergence of the backpropagation algorithm; however, this deficiency has been removed. Despite this innovation, the backpropagation method is still not without its drawbacks. Among these the most prominent are the facts that i) the learning is conducted in a supervised manner and ii) that learning and operation must occur in two distinct phases. Because of these properties, the backpropagation algorithm falls short of solving a 'true' pattern classification problem. This is not to say that a network could not be trained via backpropagation to mimic a previously solved pattern classification scheme; but that the backpropagation method is not capable of autonomously generating classification schemes. A more realistic (and certainly more useful) learning scenario is that patterns would be presented without supervision to the system continuously; consequently, the system will begin to group like patterns into similar classes and continue to do so indefinitely; i.e. continuous learning. It is exactly this type of learning that is discussed here.