A Proposal For Indicating Quality Of Generalization When Evaluating Anns
Published In
1990 IJCNN International Joint Conference on Neural Networks
Document Type
Citation
Publication Date
10-15-2012
Abstract
An expression is proposed to serve as a model for indicating quality of generalization when evaluating ANNs (analog 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.
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DOI
10.1109/IJCNN.1990.137652
Persistent Identifier
https://archives.pdx.edu/ds/psu/37264
Citation Details
Lendaris, G. G. (1990, June). A proposal for indicating quality of generalization when evaluating ANNs. In 1990 IJCNN International Joint Conference on Neural Networks (pp. 709-713). IEEE.