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Abstract: Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to... (Update)
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BibTeX entry: (Update)
H. Zhu and R. Rohwer. Information geometric measurements of generalisation. Technical Report NCRG/4350, Dept. Comp. Sci. & Appl. Math., Aston University, August 1995. ftp://cs.aston.ac.uk/neural/zhuh/generalisation.ps.Z. http://citeseer.ist.psu.edu/zhu95information.html More
@misc{ zhu95information,
author = "H. Zhu and R. Rohwer",
title = "Information geometric measurements of generalisation",
text = "H. Zhu and R. Rohwer. Information geometric measurements of generalisation.
Technical Report NCRG/4350, Dept. Comp. Sci. & Appl. Math., Aston University,
August 1995. ftp://cs.aston.ac.uk/neural/zhuh/generalisation.ps.Z.",
year = "1995",
url = "citeseer.ist.psu.edu/zhu95information.html" }
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