Lec2 - Nature Inspired Computation and Applications...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
Pattern Recognition Lecture 2 Bayesian Decision Theory Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Nature Inspired Computation and Applications Laboratory 1 Feb. 26th, 2011
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
主要内容 2 基本问题 贝叶斯决策 分类器、判别函数和判定面 正态密度 正态分布的判别函数 最大似然估计
Background image of page 2
基本问题 3 假设要识别的模式x表示为一个d 维特征向量 该样本可能属于k个类别 (c 1 ,c 2 ,…,c k ) 中的一个 怎样分类x最合理? 最小误差率分类 最小风险分类 如果我们已知下列信息,就可以用贝叶斯决策理论进行分类 各类别出现的先验概率 P (c i ) 各类别条件概率密度函数 p ( x | c i )
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
贝叶斯决策 4 利用贝叶斯公示计算后验概率 表示出现分类错误的概率,则将x分到对应 最大后验概率的类是令
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 10

Lec2 - Nature Inspired Computation and Applications...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online