Abstract
Supervised neural networks belong to the class of parameterised non-linear models whose optimum values are determined by a best fit procedure to training data. The problem of over-fitting can occur when more parameters than needed are included in the model. David MacKay’s Occam factor deploys a Bayesian approach to the investigation of how model order can be rationally restrained. This paper uses a case study to show how the Occam factor might be used to discriminate on model order and how it compares with similar indices e.g. Schwarz’s Bayesian information criterion and Akaike’s information criterion.
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References
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Booth, D.J., Tye, R. (2009). An Application of the Occam Factor to Model Order Determination. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_41
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DOI: https://doi.org/10.1007/978-3-642-03969-0_41
Publisher Name: Springer, Berlin, Heidelberg
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