基本介紹
- 作品名稱：模式識別及MATLAB實現——學習與實驗指導
- 外文名稱：attern Recognition and MATLAB Implementation-Learning and Experimental Guidance
- 創作年代：2017
- 作者：郭志強
基本信息,內容簡介,目錄信息,
基本信息
模式識別及MATLAB實現——學習與實驗指導
作 譯 者：郭志強
出版時間：2017-08
千 字 數：333
版 次：01-01
頁 數：208
開 本：16開
I S B N ：9787121323737
內容簡介
本書是《模式識別及Matlab實現》主教材的配套實驗與指導，根據主教材各章內容，相應給出了實驗的具體步驟和程式代碼，包括：貝葉斯決策，機率密度函式的參數估計，非參數判別分類方法，聚類分析，特徵提取與選擇，模糊模式識別，神經網路在模式識別中的套用，模式識別的工程套用等。
目錄信息
第 1 章貝葉斯決策 ·························································································· 1
1.1 知識要點 ····························································································· 1
1.2 實驗指導 ····························································································· 7
1.2.1 基於最小錯誤率的貝葉斯決策 ························································· 7
1.2.2 最小風險判決規則 ······································································· 12
1.2.3 最大似然比判決規則 ···································································· 16
1.2.4 Neyman-Pearsen 判決 ···································································· 21
第2 章參數估計 ···························································································· 25
2.1 知識要點 ···························································································· 25
2.2 實驗指導 ···························································································· 30
2.2.1 最大似然估計 ············································································· 30
2.2.2 貝葉斯估計 ················································································ 33
2.2.3 Parzen 窗 ··················································································· 36
2.2.4 N k 近鄰估計法 ············································································ 38
第3 章非參數判別分類法 ················································································ 41
3.1 知識要點 ···························································································· 41
3.2 實驗指導 ···························································································· 44
3.2.1 兩分法 ······················································································ 44
3.2.2 兩分法的設計 ············································································· 47
3.2.3 沒有不確定區域的兩分法 ······························································ 52
3.2.4 廣義線性判別函式的設計與實現 ····················································· 56
3.2.5 感知器算法的設計/實現 ································································ 58
3.2.6 兩類問題Fisher 準則 ···································································· 62
3.2.7 基於距離的分段線性判別函式 ························································ 68
3.2.8 支持向量機 ················································································ 74
第4 章聚類分析法 ························································································· 80
4.1 知識要點 ··························································································· 81
4.2 實驗指導 ··························································································· 84
4.2.1 距離測度 ··················································································· 84
4.2.2 相似測度算法 ············································································· 90
4.2.3 基於匹配測度算法的實現 ······························································ 98
4.2.4 基於類間距離測度方法 ································································ 103
4.2.5 聚類函式準則 ············································································ 106
4.2.6 基於最近鄰規則的聚類算法 ·························································· 108
4.2.7 基於最大最小距離聚類算法的實現 ················································· 113
4.2.8 基於K-均值聚類算法實驗 ···························································· 116
第5 章特徵提取與選擇 ·················································································· 124
5.1 知識要點 ·························································································· 124
5.2 實驗指導 ·························································································· 128
5.2.1 基於距離的可分性判據 ································································ 128
5.2.2 圖像的傅立葉變換二（旋轉性質） ················································· 130
5.2.3 基於熵函式的可分性判據 ····························································· 134
5.2.4 利用類均值向量提取特徵 ····························································· 136
5.2.5 基於類平均向量中判別信息的最優壓縮的實現 ·································· 141
5.2.6 增添特徵法 ··············································································· 144
5.2.7 剔減特徵法 ··············································································· 148
5.2.8 增l 減r（算法）的設計/實現 ························································ 151
5.2.9 分支定界法（BAB 算法） ···························································· 156
第6 章模糊模式識別 ····················································································· 161
6.1 知識要點 ·························································································· 161
6.2 實驗指導 ·························································································· 163
6.2.1 最大隸屬度識別法 ······································································ 163
6.2.2 擇近原則識別法 ········································································· 167
6.2.3 基於模糊等價關係的聚類算法研究 ················································· 170
第7 章數字圖像處理的基礎 ··········································································· 179
7.1 知識要點 ·························································································· 179
7.2 實驗指導 ·························································································· 181
7.2.1 前饋神經網路感知器的設計實現 ··················································· 181
7.2.2 基於BP 網路的多層感知器 ·························································· 184
7.2.3 自組織特徵映射網路的設計/實現 ·················································· 189
7.2.4 徑向基神經網路 ········································································ 194
參考文獻 ······································································································· 198
