Sequential classification by perceptrons and application to net pruning of multilayer perceptron

Kou-Yuan Huang*

*Corresponding author for this work

Research output: Contribution to conferencePaper

Abstract

Using the important property of the approximating a posteriori probability functions of the classes in the outputs of the trained multilayer perceptrons, we propose the technique for the implementation of sequential classification by perceptron and multilayer perceptron, and application to the node growing in the number of input nodes of perceptron and the number of hidden nodes of the multilayer perceptron. A measurement for the ordering of hidden nodes of the trained multilayer perceptron is also proposed. The ordering of the hidden nodes comes from the contribution of each hidden node. Using the node growing technique, the minimum number of hidden nodes can be obtained in the training and used in the classification. The technique can also apply to the single layer perceptron. In the experiment, the typical 'XOR' problem is applied. The balance between the reduction of hidden nodes and classification results is quite good.

Original languageEnglish
Pages561-566
Number of pages6
StatePublished - 1 Dec 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period27/06/9429/06/94

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    Huang, K-Y. (1994). Sequential classification by perceptrons and application to net pruning of multilayer perceptron. 561-566. Paper presented at Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, .