Title :
A proposal for indicating quality of generalization when evaluating ANNs
Author :
Lendaris, George
Abstract :
An expression is proposed to serve as a model for indicating quality of generalization when evaluating ANNs (artificial neural networks). A conjecture made by G.G. Lendaris and G.L. Stanley (Inf. Syst. Sci.; Proc. 2nd Congress, Baltimore Spartan Books, 1965) is repeated which predicts that if an ANN successfully learns a training set, then the smaller the ANN´s performance space, the better will be its generalization. It is argued that the chances of an ANN learning a given task are enhanced if a significant fraction of the possible inputs from the ANN´s input space is in the don´t-care set
Keywords :
learning systems; neural nets; performance evaluation; ANN learning; artificial neural networks; generalization; neural net performance; performance space; supervised learning; training set;
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/IJCNN.1990.137652
