Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som that offers possibilities to overcome this difficulty. It is shown that networks trained with BP-SOM show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction.
|Titel||Machine learning : ECML-98 : 10th European conference on machine learning, Chemnitz, Germany, April 21-23, 1998 : proceedings|
|Redacteuren||C. Nédellec, C. Rouveirol|
|Plaats van productie||Berlin|
|ISBN van geprinte versie||978-3-540-64417-0|
|Status||Gepubliceerd - 1998|
|Naam||Lecture notes in artificial intelligence|