博碩士論文 etd-0721106-213119 詳細資訊


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姓名 詹壹詔(Yi-Chao Chan) 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 94學年第2學期
論文名稱(中) 支援向量迴歸方法的參數預測
論文名稱(英) Estimation of Parameters in Support Vector Regression
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    摘要(中) 在支援向量機(support vector machine)的領域裡,核函數(kernel function)的選擇和修改是非常重要的研究,核函數對支援向量機的效能有著顯著的影響。核函數能將資料集從原本的資料空間投射到特徵空間,讓原本無法在低維度中處理的問題透過核函數的轉換,在高維度中處理,而最常用的核函數便是Radial Basis Function(RBF)。在這篇論文中,我們使用Fuzzy C-means這個群聚(clustering)的演算法將資料樣本分成數個群聚(cluster),然後使用統計的方法針對每一個資料樣本算出標準差(standard deviation),所以,我們可以得到資料的分佈並給資料樣本適當的標準差,也就是Radial Basis Function裡頭的變異數(variance),最後再將資料樣本和相對應的變異數交由支援向量機訓練。由實驗結果得知,我們的方法可以得到更好的核函數,而且也有更好的學習(learning)能力和泛化(generalization)能力。
    摘要(英) The selection and modification of kernel functions is a very important problem in the field of support vector learning. However, the kernel function of a support vector machine has great influence on its performance. The kernel function projects the dataset from the original data space into the feature space, and therefore the problems which couldn’t be done in low dimensions could be done in a higher dimension through the transform of the kernel function. In this thesis, we adopt the FCM clustering algorithm to group data patterns into clusters, and then use a statistical approach to calculate the standard deviation of each pattern with respect to the other patterns in the same cluster. Therefore we can make a proper estimation on the distribution of data patterns and assign a proper standard deviation for each pattern. The standard deviation is the same as the variance of a radial basis function. Then we have the origin data patterns and the variance of each data pattern for support vector learning. Experimental results have shown that our approach can derive better kernel functions than other methods, and also can have better learning and generalization abilities.
    關鍵字(中)
  • 模糊c均值法
  • 支援向量機
  • 序列最小優化
  • 資源分配網路
  • 輻射半徑基底函數
  • 關鍵字(英)
  • Sequential Minimal Optimization
  • Fuzzy C-means
  • RAN
  • Radial Basis Function
  • Support Vector Machine
  • 論文目次 摘要 i
    Abstract ii
    第一章 簡介 - 1 -
    第二章 支援向量機 - 5 -
    2.1 線性可分割的分類問題 - 5 -
    2.2 線性分類支援向量機 - 9 -
    2.3 軟性邊界 - 12 -
    2.4 核函數 - 15 -
    2.5 支援向量迴歸 - 20 -
    第三章 研究方法 - 27 -
    3.1 研究動機 - 27 -
    3.2 研究方法 - 30 -
    3.3 序列最小優化 - 35 -
    3.4 工作集的選擇 - 38 -
    3.5 最大違反對 - 39 -
    3.6 使用二階資訊的工作集選擇方法 - 40 -
    3.7 考慮變異數的工作集選擇方法 - 43 -
    第四章 實驗結果與分析 - 46 -
    第五章 結論 - 58 -
    參考文獻 - 59 -
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    口試委員
  • 郭忠民 - 召集委員
  • 吳志宏 - 委員
  • 歐陽振森 - 委員
  • 黃宗傳 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2006-07-04 繳交日期 2006-07-21

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