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Machine Learning
book

Machine Learning

by Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Mohammed Bashier
August 2016
Intermediate to advanced content levelIntermediate to advanced
204 pages
3h 51m
English
CRC Press
Content preview from Machine Learning

Chapter 4

Naïve Bayesian Classification

4.1 Introduction

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:

P(A|B)=P(A)P(B|A)P(B)

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 ...

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Publisher Resources

ISBN: 9781315354415