This document discusses Bayesian probability and its application to text classification. It explains how to calculate the probability P(C|D) that a document D belongs to a class C using Bayes' theorem. Key terms like bag-of-words, term frequency, and maximum likelihood estimation are also introduced. Several examples are provided to illustrate how to classify documents into different topics based on word probabilities.
This document discusses Bayesian probability and its application to text classification. It explains how to calculate the probability P(C|D) that a document D belongs to a class C using Bayes' theorem. Key terms like bag-of-words, term frequency, and maximum likelihood estimation are also introduced. Several examples are provided to illustrate how to classify documents into different topics based on word probabilities.
