《模式识别及MATLAB实现——学习与实验指导》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。

第 1 章贝叶斯决策 ·························································································· 1

1.1 知识要点 ····························································································· 1

1.2 实验指导 ····························································································· 7

1.2.1 基于最小错误率的贝叶斯决策 ························································· 7

1.2.2 最小风险判决规则 ······································································· 12

1.2.3 最大似然比判决规则 ···································································· 16

1.2.4 Neyman-Pearsen 判决 ···································································· 21

第2 章参数估计 ···························································································· 25

2.1 知识要点 ···························································································· 25

2.2 实验指导 ···························································································· 30

2.2.1 最大似然估计 ············································································· 30

2.2.2 贝叶斯估计 ················································································ 33

2.2.3 Parzen 窗 ··················································································· 36

2.2.4 N k 近邻估计法 ············································································ 38

第3 章非参数判别分类法 ················································································ 41

3.1 知识要点 ···························································································· 41

3.2 实验指导 ···························································································· 44

3.2.1 两分法 ······················································································ 44

3.2.2 两分法的设计 ············································································· 47

3.2.3 没有不确定区域的两分法 ······························································ 52

3.2.4 广义线性判别函式的设计与实现 ····················································· 56

3.2.5 感知器算法的设计/实现 ································································ 58

3.2.6 两类问题Fisher 準则 ···································································· 62

3.2.7 基于距离的分段线性判别函式 ························································ 68

3.2.8 支持向量机 ················································································ 74

第4 章聚类分析法 ························································································· 80

4.1 知识要点 ··························································································· 81

4.2 实验指导 ··························································································· 84

4.2.1 距离测度 ··················································································· 84

4.2.2 相似测度算法 ············································································· 90

4.2.3 基于匹配测度算法的实现 ······························································ 98

4.2.4 基于类间距离测度方法 ································································ 103

4.2.5 聚类函式準则 ············································································ 106

4.2.6 基于最近邻规则的聚类算法 ·························································· 108

4.2.7 基于最大最小距离聚类算法的实现 ················································· 113

4.2.8 基于K-均值聚类算法实验 ···························································· 116

第5 章特徵提取与选择 ·················································································· 124

5.1 知识要点 ·························································································· 124

5.2 实验指导 ·························································································· 128

5.2.1 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） ················································· 130

5.2.3 基于熵函式的可分性判据 ····························································· 134

5.2.4 利用类均值向量提取特徵 ····························································· 136

5.2.5 基于类平均向量中判别信息的最优压缩的实现 ·································· 141

5.2.6 增添特徵法 ··············································································· 144

5.2.7 剔减特徵法 ··············································································· 148

5.2.8 增l 减r（算法）的设计/实现 ························································ 151

5.2.9 分支定界法（BAB 算法） ···························································· 156

第6 章模糊模式识别 ····················································································· 161

6.1 知识要点 ·························································································· 161

6.2 实验指导 ·························································································· 163

6.2.1 最大隶属度识别法 ······································································ 163

6.2.2 择近原则识别法 ········································································· 167

6.2.3 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 ·························································································· 179

7.2 实验指导 ·························································································· 181

7.2.1 前馈神经网路感知器的设计实现 ··················································· 181

7.2.2 基于BP 网路的多层感知器 ·························································· 184

7.2.3 自组织特徵映射网路的设计/实现 ·················································· 189

7.2.4 径向基神经网路 ········································································ 194

参考文献 ······································································································· 198