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Projeto Descrição

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.

System Requirements

System requirement is not defined
Information regarding Project Releases and Project Resources. Note that the information here is a quote from Freecode.com page, and the downloads themselves may not be hosted on OSDN.

2005-01-03 22:26
2.0.3

Um bug crítico causando uma memória inválida a ler bnr_hash_destroy () foi corrigido.
Tags: Minor bugfixes
A critical bug causing an invalid memory read on bnr_hash_destroy() has been fixed.

2005-01-02 09:47
2.0.2

Algumas pequenas alterações para a API foram feitas para acomodar as necessidades de alguns filtros. Alguns símbolos também foram renomeados para evitar conflitos com outras bibliotecas.
Tags: Minor bugfixes
Some minor changes to the API were made to accommodate
needs by some filters. Some symbols were also renamed to
avoid conflict with other libraries.

2004-12-29 04:47
2.0.0

Esta versão utiliza um método puramente estatísticos de redução de ruído utilizando um padrão de aprendizagem e aproximação verificação de consistência. Padrões de p-valor tuplas são gerados e aprendeu como metatokens dentro do classificador. A disposição dos padrões são então comparados com os p-valores dos símbolos incluídos no padrão. Qualquer inconsistência superior a um raio de exclusão em seguida, são eliminados como ruído.
Tags: Major feature enhancements
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
noise.

2004-07-26 16:39
1.2

Alguns bugs lançamento inicial no algoritmo foram reparados. O código foi atualizado para v1.2 do algoritmo.
Tags: Major bugfixes
Some initial release bugs in the algorithm were
repaired. The code was upgraded to v1.2 of the
algorithm.

2004-07-22 19:12
1.0.0

Tags: Initial freshmeat announcement

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