Refereed full papers (journals, book chapters, international conferences)

1993

@inproceedings{PMD1993,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{Erich Prem and Markus Mackinger and Georg Dorffner},
	title =	{Concept Support as a Method for Programming Neural
	Networks with Symbolic Knowledge},
	booktitle =	{Proceedings of the German Artificial Intelligence
	Conference},
	address =	{Berlin-Heidelberg},
	publisher =	{Springer Verlag},
	year =	{1993},
	abstract =	{Neural networks are usually seen as obtaining all their
	knowledge through training on the basis of examples. In many AI
	applications appropriate for neural networks, however, symbolic
	knowledge does exist which describes a large number of cases relatively
	well, or at least contributes to partial solutions. From a practical
	point of view it appears to be a waste of resources to give up this
	knowledge altogether by training a network from scratch. This paper
	introduces a method for inserting symbolic knowledge into a neural
	network -- called ``concept support''. This method is non-intrusive in
	that it does not rely on immediately setting any internal variable,
	such as weights. Instead, knowledge is inserted through pre-training on
	concepts or rules believed to be essential for the task. Thus the
	knowledge actually accessible for the neural network remains
	distributed or -- as it is called -- subsymbolic.  Results from a test
	application are reported which show considerable improvements in
	generalization.},
}