Abstract
It is well-known that the accuracy of a neural network classifier increases as the number of data points in the training set increases. A previous researcher has proposed a general mathematical model that explains the relationship between training sample size and predictive power. We examine this model using artificially generated data sets containing varying numbers of data points and some real world data sets. We find the model works well when large numbers of data points are available for training, but presents practical difficulties when the amount of available data is small and the data set is difficult to classify.
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Crowther, P.S., Cox, R.J. (2006). Accuracy of Neural Network Classifiers as a Property of the Size of the Data Set. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_144
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DOI: https://doi.org/10.1007/11893011_144
Publisher Name: Springer, Berlin, Heidelberg
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