Abstract
Neural Networks are used to find a generalised solution from a sample set of a problem domain. When a small sample is all that is available, the correct division of data between the training, testing and validation sets is crucial to the performance of the resultant trained network. Data is often divided uniformly between the three data sets. We propose an alternative method for the optimal division of the data, based on empirical evidence from experiments with artificial data. The method is tested on real world data sets, with encouraging results. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Crowther, P. S., & Cox, R. J. (2005). A method for optimal division of data sets for use in neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3684 LNAI, pp. 1–7). Springer Verlag. https://doi.org/10.1007/11554028_1
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