Reviews: 0 ... further results.
Implementation of the Bayesian Noise Reduction (BNR) algorithm
'libbnr' implements of the Bayesian Noise Reduction (BNR) algorithm. All samples of text contain some degree of noise (data irrelevant to accurate statistical analysis of the sample where removal of the data would result in a cleaner analysis). The Bayesian noise reduction algorithm ensures cleaner machine learning by providing more useful data, which ultimately leads to better sample analysis. With the noisy data removed from the sample, only data relevant to the classification is left. 'libbnr' can be linked in with a classifier and called using the standard C interface.
released on 26 July 2004
22 July 2004
Leaders and contributors
|Jonathan A. Zdziarski||Maintainer|
Resources and communication
This entry (in part or in whole) was last reviewed on 21 January 2017.
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.
The copyright and license notices on this page only apply to the text on this page. Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself.