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 广义线性判别函数的设计与实现 ····················································· 56
3.2.5 感知器算法的设计/实现 ································································ 58
3.2.6 两类问题Fisher 准则 ···································································· 62
3.2.7 基于距离的分段线性判别函数 ························································ 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 聚类函数准则 ············································································ 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 章特征提取与选择 ·················································································· 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 图像的傅里叶变换二（旋转性质） ················································· 130
5.2.3 基于熵函数的可分性判据 ····························································· 134
5.2.4 利用类均值向量提取特征 ····························································· 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 增添特征法 ··············································································· 144
5.2.7 剔减特征法 ··············································································· 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 基于模糊等价关系的聚类算法研究 ················································· 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 前馈神经网络感知器的设计实现 ··················································· 181
7.2.2 基于BP 网络的多层感知器 ·························································· 184
7.2.3 自组织特征映射网络的设计/实现 ·················································· 189
7.2.4 径向基神经网络 ········································································ 194
参考文献 ······································································································· 198