libbnr is an implementation of the Bayesian Noise Reduction (BNR) algorithm. All samples of text contain some degree of noise (data which is either intentionally or unintentionally irrelevant to accurate statistical analysis of the sample where removal of the data would result in a cleaner analysis). The Bayesian noise reduction algorithm provides a means of cleaner machine learning by providing more useful data, which ultimately leads to better sample analysis. With the noisy data removed from the sample, what is left is only data relevant to the classification. libbnr can be linked in with your classifier and called using the standard C interface.
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This version employs a purely statistical method of noise
reduction using a pattern learning and consistency checking
approach. Patterns of p-value tuples are generated and
learned as metatokens within the classifier. The disposition
of patterns are then compared against the p-values of the
tokens included in the pattern. Any inconsistencies
exceeding an exclusionary radius are then eliminated as