On Biased Learning for Generalisation
Mikael Bodén · 1994
Pure exemplar-based learning suffers from the inability to choose an appropriate generalisation out of many possible, where each is correct with respect to the learning examples. In connectionist terms; learning is not necessarily successful for input data not included in the learning examples even if the learning error is minimized. Ideas from research in machine learning, artificial intelligence and connectionism are used to develop intuitions for a biased approach where knowledge about the type, its future use and simplicity of the implementation may play a part in deciding which generalisation to prefer. 1 Motivation Many connectionists have concentrated on learning as if it was a matter of detecting and discovering statistical variances and regularities. In this paper a different standpoint is taken. It is argued that the detection of regularities is not sufficient to guarantee success. Parametric and data based methods are inherently not intelligent. It is argued that we need kn...