August 2016
Intermediate to advanced
204 pages
3h 51m
English
Naïve Bayesian classifiers [1] are simple probabilistic classifiers with their foundation on application of Bayes’ theorem with the assumption of strong (naïve) independence among the features. The following equation [2] states Bayes’ theorem in mathematical terms:
where:
A and B are events
P(A) and P(B) are the prior probabilities of A and B without regard to each other
P(A|B), also called posterior probability, is the probability of observing event A given that B is true
P(B|A), also called likelihood, is the probability of observing event B given that A is true
Suppose that vector X = (x1, x2, … xn) is an instance (with n independent features) to be classified ...