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Naive Bayesian tutorial for ML
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Naive bayesian
1.
朴素贝叶斯分类器 • 决策树、神经网络、最近邻方法之外,最 实用的学习方法 • 适用性: 中等或大规模数据学习 实例属性有条件地独立于给定分类
成功应用 故障诊断 文本分类
2.
朴素贝叶斯分类器 • 假定目标函数: • 样例属性: •
最可能的分类值为: V X f : n a a a a ,..., , , 3 2 1
3.
朴素贝叶斯分类器 • 朴素贝叶斯假定: 得到贝叶斯分类器: 各属性分类结 果独立
4.
朴素贝叶斯分类算法 • 朴素贝叶斯学习 对于每个目标值vj: 对于每个属性值: 分类新样例:
5.
朴素贝叶斯分类例 • 考虑Playtennis 计算:
6.
朴素贝叶斯分类例 • 考虑Playtennis 计算:
7.
朴素贝叶斯:细节 • 条件独立假设约束强 不必估计后验概率: 仅需要计算:
8.
Homework •Implement one function
for naï ve bayesian, which can be done by any kind of language •The samples can be the one provided in class •Students should search some applications of naï ve bayesian
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