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
![分類器 (ナイーブベイズ)](https://cdn-ak-scissors.b.st-hatena.com/image/square/7e54fae175182976b385e9bbea632a8a5592afa0/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Fbaysematsuura-110526051004-phpapp01-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)