The nave bayes classier involves a learning step

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Unformatted text preview: e specific order of the words in a document is not very important. Even more we may assume that for documents of a given class a word appears in the document irrespective of the presence of other words. This leads to a simple formula for the conditional probability of words given a class Lc p ( t1 , . . . , t ni | L c ) = ni ∏ p(t j | Lc ) j =1 Combining this “naïve” independence assumption with the Bayes formula defines the Naïve Bayes classifier (Good 1965). Simplifications of this sort are required as many thousand different words occur in a corpus. The naïve Bayes classifier involves a learning step which simply requires the estimation of the probabilities of words p(t j | Lc ) in each class by its relative frequencies in the documents of a training set which are labelled with Lc . In the classification step the estimated probabilities are used to classify a new instance according to the Bayes rule. In order to reduce the number of probabilities p(t j | Lm ) to be estimated, we can...
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