Note that P(d18) is the same for all classes, thus d can be classified by computing Cd = argmax P(ej 0)P(d cj; 0) (4) ct EC The multi-variate Bernoulli event model assumes that a document is generated by a series of VI Bernoulli experiments, one for each word wt in the vocabulary V. The outcome of each experiment determines whether the corresponding word will be included at least once in the document.
a document di can be represented as a binary feature vector of length I V. where each dimension t of the vector, denoted as Bit c {0, 1}, indicates whether word wt occurs at least once in dz .