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Abstract: Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalising over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a `moderation' of the most probable classifier 's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in... (Update)
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BibTeX entry: (Update)
MacKay, D. J. C. 1992a. The evidence framework applied to classification networks. Neural Computation 4. http://citeseer.ist.psu.edu/mackay92evidence.html More
@article{ mackay92evidence,
author = "MacKay, D.",
title = "The Evidence Framework Applied to Classification Networks",
journal = "Neural Computation",
volume = "4",
number = "5",
pages = "720--736",
year = "1992",
url = "citeseer.ist.psu.edu/mackay92evidence.html" }
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58 Statistical mechanics of learning from examples (context) - Seung, Sompolinsky et al. - 1991
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