Naive Bayesian


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IntelligenceLab VC++  v.5.0.3

IntelligenceLab is a set of Visual C++ components for Artificial Intelligence - Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor

Bayesian Message Filter  v.2

Public domain portable Naive Bayesian message filter aimed towards C programmers.





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IntelligenceLab .NET  v.6.0

Set of .NET 2.0-4.5 Artificial Intelligence components - Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor. Includes a visual graphical editor for codeless development. Free for non-commercial use

IntelligenceLab VCL  v.7.0

Delphi/C++ Builder VCL/FMX components library for Artificial Intelligence components - Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor. Includes visual graphical editor. Free for non-commercial

RDP Classifier  v.2.4

The RDP Classifier is a naive Bayesian classifier that can rapidly and accurately provides taxonomic assignments from domain to genus, with confidence estimates for each assignment.

Bayesian Filter Library  v.0.2.0beta

A general purpose C++ library for Bayesian Filtering.

Bayesian Network tools in Java (BNJ)  v.2006

Bayesian Network tools in Java (BNJ) is an open-source suite of software tools for research and development using graphical models of probability.

Bayesian Surprise Toolkit for Matlab  v.0.15b

Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples.

Bogofilter -- Fast Bayesian Spam Filter  v.1.2.2

The bogofilter package implements a fast Bayesian spam filter assuggested by Paul Graham in "A Plan For Spam"

Dbacl - digramic Bayesian classifier  v.1.12

dbacl is a general purpose digramic Bayesian text classifier.

SpamProbe - fast bayesian spam filter  v.1.4

Fast, intelligent, automatic spam detector using Paul Graham style Bayesian analysis of word counts in spam and non-spam emails.

Bayesian Calculator  v.2.0

Calculates positive predictive power, given prevalence and test results.

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