模式识别及MATLAB实现：学习与实验指导 _生活百科

模式识别及MATLAB实现：学习与实验指导基本介绍作品名称：模式识别及MATLAB实现——学习与实验指导
外文名称：attern Recognition and MATLAB Implementation-Learning and Experimental Guidance
创作年代：2017
作者：郭志强
基本信息模式识别及MATLAB实现——学习与实验指导作译者：郭志强出版时间：2017-08千字数：333版次：01-01页数：208开本：16开I S B N ：9787121323737内容简介本书是《模式识别及Matlab实现》主教材的配套实验与指导，根据主教材各章内容，相应给出了实验的具体步骤和程式代码，包括：贝叶斯决策，机率密度函式的参数估计，非参数判别分类方法，聚类分析，特徵提取与选择，模糊模式识别，神经网路在模式识别中的套用，模式识别的工程套用等。目录信息第 1 章贝叶斯决策 ·························································································· 11.1 知识要点 ····························································································· 11.2 实验指导 ····························································································· 71.2.1 基于最小错误率的贝叶斯决策 ························································· 71.2.2 最小风险判决规则 ······································································· 121.2.3 最大似然比判决规则 ···································································· 161.2.4 Neyman-Pearsen 判决 ···································································· 21第2 章参数估计 ···························································································· 252.1 知识要点 ···························································································· 252.2 实验指导 ···························································································· 302.2.1 最大似然估计 ············································································· 302.2.2 贝叶斯估计 ················································································ 332.2.3 Parzen 窗 ··················································································· 362.2.4 N k 近邻估计法 ············································································ 38第3 章非参数判别分类法 ················································································ 413.1 知识要点 ···························································································· 413.2 实验指导 ···························································································· 443.2.1 两分法 ······················································································ 443.2.2 两分法的设计 ············································································· 473.2.3 没有不确定区域的两分法 ······························································ 523.2.4 广义线性判别函式的设计与实现 ····················································· 563.2.5 感知器算法的设计/实现 ································································ 583.2.6 两类问题Fisher 準则 ···································································· 623.2.7 基于距离的分段线性判别函式 ························································ 683.2.8 支持向量机 ················································································ 74 第4 章聚类分析法 ························································································· 804.1 知识要点 ··························································································· 814.2 实验指导 ··························································································· 844.2.1 距离测度 ··················································································· 844.2.2 相似测度算法 ············································································· 904.2.3 基于匹配测度算法的实现 ······························································ 984.2.4 基于类间距离测度方法 ································································ 1034.2.5 聚类函式準则 ············································································ 1064.2.6 基于最近邻规则的聚类算法 ·························································· 1084.2.7 基于最大最小距离聚类算法的实现 ················································· 1134.2.8 基于K-均值聚类算法实验 ···························································· 116第5 章特徵提取与选择 ·················································································· 1245.1 知识要点 ·························································································· 1245.2 实验指导 ·························································································· 1285.2.1 基于距离的可分性判据 ································································ 128 5.2.2 图像的傅立叶变换二（旋转性质） ················································· 1305.2.3 基于熵函式的可分性判据 ····························································· 1345.2.4 利用类均值向量提取特徵 ····························································· 1365.2.5 基于类平均向量中判别信息的最优压缩的实现 ·································· 1415.2.6 增添特徵法 ··············································································· 1445.2.7 剔减特徵法 ··············································································· 1485.2.8 增l 减r（算法）的设计/实现 ························································ 1515.2.9 分支定界法（BAB 算法） ···························································· 156第6 章模糊模式识别 ····················································································· 1616.1 知识要点 ·························································································· 1616.2 实验指导 ·························································································· 1636.2.1 最大隶属度识别法 ······································································ 1636.2.2 择近原则识别法 ········································································· 1676.2.3 基于模糊等价关係的聚类算法研究 ················································· 170第7 章数字图像处理的基础 ··········································································· 179 7.1 知识要点 ·························································································· 1797.2 实验指导 ·························································································· 1817.2.1 前馈神经网路感知器的设计实现 ··················································· 1817.2.2 基于BP 网路的多层感知器 ·························································· 1847.2.3 自组织特徵映射网路的设计/实现 ·················································· 1897.2.4 径向基神经网路 ········································································ 194参考文献 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