Naive Bayes spam filtering - Wikipedia
Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag-of-words features to identify email spam, an approach commonly used in text classification. Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes' theorem to calculate a probability that an
Random forest - Wikipedia
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees.[1][2] Random forests correct for d
Naive Bayes classifier - Wikipedia
Example of a naive Bayes classifier depicted as a Bayesian Network In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.[1] Naive
Bayesian inference - Wikipedia
Toggle Inference over exclusive and exhaustive possibilities subsection
Bayesian probability - Wikipedia
Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən)[1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation[2] representing a state of knowledge[3] or as quantification of a personal belief.[4] The Bayesian interpretation of probability can be seen as an extension of
Bayesian average - Wikipedia
A Bayesian average is a method of estimating the mean of a population using outside information, especially a pre-existing belief,[1] which is factored into the calculation. This is a central feature of Bayesian interpretation. This is useful when the available data set is small.[2] Calculating the Bayesian average uses the prior mean m and a constant C. C is chosen based on the typical data set s
DivmodReverend - Divmod - Trac
Divmod : Reverend Reverend is a general purpose Bayesian classifier, named after Rev. Thomas Bayes. Use the Reverend to quickly add Bayesian smarts to your app. To use it in your own application, you either subclass Bayes or pass it a tokenizing function. Bayesian fun has never been so quick and easy. Many thanks for Christophe Delord for his well written PopF. Orange also looks good. If you are l
linkiblog | How to Build a Popularity Algorithm You can be Proud of
<< Asp.net Localization: How to Access a Local Resource from Outside the Page | Many web sites allow users to casts vote on items. These visitors' votes are then often used to detect the items' "popularity" and hence rank the rated items accordingly. And when "rank" comes into play things gets tricky: The system can have inherent deficiencies in ranking items. That is mostly because develope
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http://www.mblondel.org/journal/2010/06/21/semi-supervised-naive-bayes-in-python/
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