Title

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.

DOI

10.1109/IJCNN.1990.137652

Persistent Identifier

https://archives.pdx.edu/ds/psu/37264

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