姓名 彭泓文(Hung-Wen Peng) 電子郵件信箱 E-mail 資料不公開 畢業系所 電機工程學系研究所(Electrical Engineering) 畢業學位 碩士(Master) 畢業時期 103學年第1學期 論文名稱(中) 結合混合式學習的自構式旋轉相似度演算法用於分類與回歸問題 論文名稱(英) A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems 檔案
紙本論文：5 年後公開 (2019-12-30 公開)
電子論文：使用者自訂權限：校內 5 年後、校外 5 年後公開
論文語文/頁數 中文/64 統計 本論文已被瀏覽 5628 次，被下載 52 次 摘要(中) 針對單標籤分類、多標籤分類與回歸預測的問題，我們提出了一個演算法來解決這些問題，這個演算法主要可以分成四個步驟，分別是旋轉相似度計算、加權關聯性計算、混合式學習與門檻值檢測。
摘要(英) We propose an algorithm for single label classification, multi-label classification, and regression estimation which incorporates a rotating similarity, weighted relevance, hybrid learning, and threshold checking.
Firstly, the rotating cluster similarity is more suitable of the distribution of the data set with hyper-spherical, hyper-ellipsoidal, or oblique hyper-ellipsoidal shapes and it is used to transform each input instance into a rotating cluster similarity. Then, the similarity of the input instance will be combined to obtain the weighted relevance of the input instance to each particular category or output value. Next, we use the hybrid learning method to refine the parameters which is in this algorithm to get better performance. Finally, the threshold checking is used to obtain the output. We will set different kind of threshold functions to determine the output due to the kind of problems.
The number of rotating clusters do not need to be specified in advance. Each cluster will self-construct during the training phase. A number of experimental results are shown the effectiveness of our proposed method.
關鍵字(中) 加權關聯性 旋轉群相似度 混合式學習 回歸預測 分類問題 關鍵字(英) weighted relevance rotating cluster similarity hybrid learning regression estimation classification 論文目次 致謝 i
第一章 導論 1
1.1. 研究動機與文獻回顧 1
1.2. 問題描述 4
1.3. 論文架構 4
第二章 文獻探討 5
2.1. 主成分分析(PCA) 5
2.2. Versatile elliptic basis function neural network (VEBF) 7
2.2.1. VEBF架構與概述 7
2.2.2. Geometrical Growth Criterion 8
2.2.3. Merging Strategy 10
2.3. LO Method 11
2.3.1. Structure Identification 13
2.3.2. Parameter Identification 15
第三章 研究方法 18
3.1. 系統流程與架構 18
3.2. 旋轉群相似度計算(Rotating Cluster Similarity) 20
3.2.1. 自構性規範(Self-Constructing Criterion) 21
3.3. 加權關聯性計算(Weighted Relevance) 25
3.4. 混合式學習法(Hybrid Learning) 27
3.5. 門檻值檢測(Threshold Checking) 29
3.6. 演算法與時間複雜度分析(Time Complexity Analysis) 30
3.7. 範例說明 32
第四章 實驗結果與分析 38
4.1. 單標籤分類 38
4.2. 多標籤分類 41
4.3. 回歸預測 43
第五章 結論與未來研究方向 47
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口試委員 歐陽振森 - 召集委員
劉志峰 - 委員
林永申 - 委員
潘欣泰 - 委員
李錫智 - 指導教授
口試日期 2014-12-10 繳交日期 2014-12-30