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Lec3 - Nature Inspired Computation and Applications...

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Nature Inspired Computation and Applications Laboratory Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Pattern Recognition Lecture 3 Non-Parametric Method Mar. 5th, 2011
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Nature Inspired Computation and Applications Laboratory 主要内容 引言 概率密度的估计 Parzen 窗方法 K n -近邻估计 • 最近邻规则 • 距离度量和最近邻分类 • 模糊分类 RCE 网络 • 级数展开逼近
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Nature Inspired Computation and Applications Laboratory 主要内容 引言 概率密度的估计 Parzen 窗方法 K n -近邻估计 • 最近邻规则 • 距离度量和最近邻分类 • 模糊分类 RCE 网络 • 级数展开逼近
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Nature Inspired Computation and Applications Laboratory 引言 前面处理有监督学习过程的条件是: 假设概率密度函数的参数形式已知 实际上:一般概率密度的形式很少符合实际情况 -所有的经典的密度函数的参数形式都是单模的,只有单个局 部极大值,而现实情况通常是多模的 -高维概率密度可以表示成一些一维密度的乘积的假设通常不 成立 引入“非参数化方法” :不必假设密度参数形式已知,能 处理任意概率分布
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Nature Inspired Computation and Applications Laboratory 非参数化方法 从训练样本中估计概率密度函数 直接估计后验概率 ) | ( j w x p ) | ( x w P j
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Nature Inspired Computation and Applications Laboratory 主要内容 引言 概率密度的估计 Parzen 窗方法 K n -近邻估计 • 最近邻规则 • 距离度量和最近邻分类 • 模糊分类 RCE 网络 • 级数展开逼近
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