Neural Network Based Classifers for a Vast Amount of Data

Authors:
Ling Zhang;Bo Zhang
Affiliations:
-;-
Venue:
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Year:
1999

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Abstract

When using neural networks to train a large number of data for classification, there generally exists a learning complexity problem. In this paper, a new geometrical interpretation of McCulloch-Pitts (M-P) neural model is presented. Based on the interpretation, a new constructive learning approach is discussed. Experimental results show that the new algorithm can greatly reduce the learning complexity and can be applied to real classification problems with a vast amount of data.