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instantiating a series of machine-generated contexts to serve as a means of contrast. This makes it possible to identify text that is out of context using a form of pattern consistency checking. BNR attempts to solve the problem commonly referred to as "Bayesian Noise" which, in its simplest definition, refers to irrelevant data present in a message being classified. Bayesian Noise Reduction dubs irrelevant text in order to provide cleaner classification and is implemented as a pre-filter to existing language classification functions. PR: ports/78159 Submitted by: Ion-Mihai "IOnut" Tetcu <itetcu@people.tecnik93.com>
16 lines
805 B
Plaintext
16 lines
805 B
Plaintext
Bayesian Noise Reduction is a statistical approach to evaluating coherence by
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instantiating a series of machine-generated contexts to serve as a means of
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contrast. This makes it possible to identify text that is out of context using
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a form of pattern consistency checking. BNR attempts to solve the problem
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commonly referred to as "Bayesian Noise" which, in its simplest definition,
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refers to irrelevant data present in a message being classified. Bayesian Noise
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Reduction dubs irrelevant text in order to provide cleaner classification and
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is implemented as a pre-filter to existing language classification functions.
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BNR is used in Dspam (mail/dspam, mail/dspam-devel - the ports don't depent on
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this one)
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See www for white-paper and presentation.
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WWW: http://www.nuclearelephant.com/papers/bnr.html
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