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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Inoue, H., Narihisa, H. (2000). Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_22
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DOI: https://doi.org/10.1007/3-540-45571-X_22
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