Injecting knowledge into the solution of the two-spiral problem
Wieland's two-spiral problem is often used as a test for comparing the quality of different supervised-learning algorithms and architectures. In this paper, we use this two-spiral problem to illustrate the advantages obtained from using all the additional knowledge about the problem domain in designing the neural net which solves a given problem. The characteristics of the knowledge-based net, with regard to complexity, number of elements, training speed and generalisation quality, make it appreciably better than alternative nets which make no use of this knowledge.
generalisation backpropagation benchmark knowledge based design neural networks two spirals of wieland