Download List

프로젝트 설명

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.

2004-12-29 04:47 Back to release list
2.0.0

이 버전은 소음 감소를 순수한 통계적 방법으로 패턴을 학습과 일관성을 확인하고 접근 방법을 사용하여 고용하고있다. 공공의 가치를 tuples 패턴 생성 및 metatokens로 분류 시간을 배웠습니다. 패턴을 처분 후 P는 비교됩니다 토큰의 값을 패턴을 포함합니다. 모든 불일치를 배제 반경을 초과하는 노이즈로 누른 다음 제거합니다.
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.

Project Resources