Abstract
We present an ensemble averaging effect for improving the generalization capability of self-generating neural networks applied to classification problems. The results of our computational experiments show that ensemble averaging effect is 1-7% improvements in accuracy comparing with single SGNN for three benchmark problems.
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CITATION STYLE
Inoue, H., & Narihisa, H. (2000). Improving generalization ability of self-generating neural networks through ensemble averaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 177–180). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_22
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