Adaptability of the backpropagation procedure

Possible paradigms for concept learning by feedforward neural networks include discrimination and recognition. An interesting aspect of this dichotomy is that the recognition-based implementation can learn certain domains much more efficiently than the discrimination-based one, despite the close structural relationship between the two systems. The purpose of this paper is to explain this difference in efficiency. We suggest that it is caused by a difference in the generalization strategy adopted by the backpropagation procedure in both cases: while the autoassociator uses a (fast) bottom-up strategy, MLP has recourse to a (slow) top-down one, despite the fact that the two systems are both optimized by the backpropagation procedure. This result is important because it sheds some light on the nature of backpropagation´s adaptive capability. From a practical viewpoint, it suggests a deterministic way to increase the efficiency of backpropagation-trained feedforward networks