Terminal Attractor Algorithms and the Class of Unimodal Loading Problems

The effectiveness of connectionist models in emulating intelligent behaviour and solving significant practical problems is strictly related to the capability of the learning algorithms to find optimal or near–optimal solutions, and to generalise to new examples. This paper deals with optimal learning and provides a unified viewpoint of most significant results in the field. We briefly review proposals for discovering optimal solutions and give some general guidelines for performing successful optimisation. Most importantly, we show some intriguing links between optimal learning and the computational complexity of loading problems. We prove that all problems giving rise to unimodal error functions have O(1) as a complexity upper bound, thus suggesting that they belong to the same class, defined on the basis of computational requirements.