Bayesian Classifier (Naive Bayesian Classifier - 朴素贝叶斯分类)
来源:互联网 发布:黑米黄牛抢购软件 编辑:程序博客网 时间:2024/05/18 00:42
•Bayes Rule
•Maximum a posteriori (MAP) hypothesis
Note P(x) is independent of h, hence can be ignored.
•Assuming that each hypothesis in H is equally probable, i.e., P(hi) = P(hj), for all i and j, then we can drop P(h) in MAP. P(d|h) is often called the likelihood of data d given h. Any hypothesis that maximizes P(d|h) is called the maximum likelihood hypothesis
(如果类的先验概率未知那我们就假设对于任意的i,j ; P(hi) = P(hj),此时我们可以不考虑 P(h),则目标函数化简如下:)
•The Bayesian approach to classifying a new instance X is to assign it to the most probable target value Y (MAP classifier)
(d为类别标示,x1,...x4为样本的各个维度的值)
(如果属性值之间不是相互独立的,那么我们不仅需要大量计算还需要一个巨大的训练样本集合,朴素贝叶斯假设样本的各个属性之间是相互独立的)
Naive Bayesian Classifier is based on the simplifying assumption that the attribute values are conditionally independent given the target value.
This means, we have
(朴素贝叶斯的目标函数)
e.g
0 0
- Bayesian Classifier (Naive Bayesian Classifier - 朴素贝叶斯分类)
- 朴素的贝叶斯分类器(Naive Bayesian Classifier)
- 朴素贝叶斯分类器(Naive Bayesian Classifier)
- Naive Bayesian Classifier(朴素贝叶斯算法)
- 贝叶斯分类器与朴素贝叶斯分类器(Naive Bayesian Classifier,NBC)
- naive bayesian classifier
- 贝叶斯分类器Bayesian Classifier
- 朴素贝叶斯分类Naive Bayesian
- bayesian classifier
- 朴素贝叶斯分类器 Naive Bayes Classifier
- 分类算法之朴素贝叶斯分类(Naive Bayesian classification)
- naive-bayesian-朴素贝叶斯
- Naive Bayesian(朴素贝叶斯)
- 朴素贝叶斯分类器(Navie Bayesian Classifier)中的几个要点(一)
- 朴素贝叶斯分类算法(Naive Bayesian classification)
- 朴素贝叶斯分类(Naive Bayesian classification)
- 朴素贝叶斯分类(Naive Bayesian classification)
- 朴素贝叶斯分类(Naive Bayesian classification)
- android消息循环
- HDU 2011 多项式求和
- linuxshell 系列 sed 命令基本用法
- 字符统计
- 运筹学学习
- Bayesian Classifier (Naive Bayesian Classifier - 朴素贝叶斯分类)
- 图像形态学处理(1)
- python struct,pickle,socket.ntohs, ntohl, htons,htonl
- Android Volley完全解析(二),使用Volley加载网络图片
- iOS面试题汇总
- android framework中添加使用第三方jar包
- 学习git的一些命令
- HMM隐Markov模型的原理及应用建模
- 浅谈BroadcastReceiver