贝叶斯分类器贝叶斯分类器的分类原理是通过某对象的先验概率,利用贝叶斯公式计算出其后验概率,即该对象属于某一类的概率,选择具有最大后验概率的类作为该对象所属的类。目前研究较多的贝叶斯分类器主要有四种,分别是:Naive Bayes、TAN、BAN和GBN。

上传者: bear_fish | 上传时间: 2019-12-21 18:48:12 | 文件大小: 67KB | 文件类型: rar
贝叶斯决策就是在不完全情报下,对部分未知的状态用主观概率估计,然后用贝叶斯公式对发生概率进行修正,最后再利用期望值和修正概率做出最优决策。   贝叶斯决策理论方法是统计模型决策中的一个基本方法,其基本思想是:   1、已知类条件概率密度参数表达式和先验概率。   2、利用贝叶斯公式转换成后验概率。   3、根据后验概率大小进行决策分类。

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评论信息

  • LHZLJT :
    正在学习贝叶斯,有用,不错
    2014-09-11
  • 郭晶 :
    不错,对初学者很有帮助谢了!
    2014-04-09
  • fanhongli511 :
    我们刚开始学,挺有帮助
    2013-05-07
  • zd_cui :
    正在学习贝叶斯分类,有点帮助
    2013-03-17

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