博碩士論文 etd-0729108-172006 詳細資訊

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姓名 蘇文彬(Wen-pin Su) 電子郵件信箱 jimisu@water.ee.nsysu.edu.tw
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 96學年第2學期
論文名稱(中) 應用多核心支援向量機於偽鈔辨識
論文名稱(英) Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition
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    論文語文/頁數 中文/64
    統計 本論文已被瀏覽 5060 次,被下載 2187 次
    摘要(中) 機器學習方法在樣式辨別領域有許多成功\的應用,其中一個有趣的主題是偽鈔辨識。許多的研究將鈔票影像切割之後,每個特徵區塊訓練出個別的單一類別支援向量機,並使用基因演算法尋找最佳的分類器組合方式;如此需要花費較高的時間成本。在本篇論文中,將每個特徵區塊建構一個多核心組合,以半定規劃求解最佳的權重係數。然後,我們改良了半定規劃用於解決多核心學習的演算法,降低問題的搜尋空間。在實際應用上偽鈔數量遠少於真鈔,所以重點在於降低偽鈔被誤判成真鈔的機率,我們加入了最小化False alarm rate的機制,提出一個加權支援向量機的方法。對於鈔票的特徵擷取,以背光板做為穩定光源凸顯真假鈔的色階分布差異,把取樣的影像切割成 的區塊,每個區塊轉換成直方圖作為特徵。實驗結果證明了這樣的特徵擷取方式是快速且有效的,而且我們的多核心支援向量機方法更適合於偽鈔辨識且更有效率。
    摘要(英) Finding an efficient method to detect counterfeit banknotes is imperative. In this study, we propose multiple kernel weighted support vector machine for counterfeit banknote recognition. A variation of SVM in optimizing false alarm rate, called FARSVM, is proposed which provide minimized false negative rate and false positive rate. Each banknote is divided into m × n partitions, and each partition comes with its own kernels. The optimal weight with each kernel matrix in the combination is obtained through the semidefinite programming (SDP) learning method. The amount of time and space required by the original SDP is very demanding. We focus on this framework and adopt two strategies to reduce the time and space requirements. The first strategy is to assume the non-negativity of kernel weights, and the second strategy is to set the sum of weights equal to 1. Experimental results show that regions with zero kernel weights are easy to imitate with today’s digital imaging technology, and regions with nonzero kernel weights are difficult to imitate. In addition, these results show that the proposed approach outperforms single kernel SVM and standard SVM with SDP on Taiwanese banknotes.
  • 支援向量機
  • 加權支援向量機
  • 多核心學習
  • 半定規劃
  • 偽鈔辨識
  • 關鍵字(英)
  • Support vector machine
  • Banknote recognition
  • Multiple kernel learning
  • Semidefinite programming
  • False alarm rate
  • Weighted support vector machine
  • 論文目次 摘要 iii
    Abstract iv
    圖目錄 v
    表目錄 vi
    第一章 導論 1
    第二章 基本理論 4
    2.1 線性分類 4
    2.1.1 最大邊界分類器 5
    2.1.2 軟性邊界分類器 10
    2.2 支援向量機 14
    2.2.1 特徵映射 14
    2.2.2 硬性邊界支援向量機 16
    2.2.3 軟性邊界支援向量機 17
    2.3 多核心學習 20
    2.3.1 核心函數的組成 21
    2.3.2 半定規劃 22
    2.3.3 半定規劃用於多核心學習 23
    第三章 研究方法 27
    3.1 改良式半定規劃 27
    3.1.1 Induction的方法 27
    3.1.2 SDPI的推導 30
    3.2 半定規劃用於偽鈔辨識 33
    3.2.1 加權支援向量機 33
    3.2.2 我們的方法 35
    3.3 樣本的前處理 37
    3.3.1 鈔票取樣 37
    3.3.2 特徵擷取 38
    第四章 實驗結果 43
    4.1單一核心的SVM與FARSVM 43
    4.2多核心的SVM與FARSVM 44
    4.3影像切割配合SDPT演算法 45
    4.4影像切割配合SDPI演算法 47
    第五章 結論與展望 51
    參考文獻 53
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    [13] Y. E. Nesterov and A. S. Nemirovsky, “Interior Point Polynomial Methods in Convex Programming: Theory and Applications,” SIAM, Vol. 13, 1994.
    [14] C. S. Ong, A. J. Smola, and R. C. Williamson, “Learning the Kernel with Hyperkernels,” Journal of Machine Learning Research, Vol. 6, pp. 1045-1071, 2005.
    [15] J.C. Russ, The Image Processing Handbook, Fifth Edition, CRC Press, 2007.
    [16] B. Schölkopf, and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA, 2002.
    [17] J. Shawe-Taylor, and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
    [18] S. Sonnenburg, G. Räetsch, C. Schäefer, and B. Schölkopf, “Large Scale Multiple Kernel Learning,” Journal of Machine Learning Research, Vol. 7, pp. 1531-1565, 2006.
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    [20] L. Vandenberghe, and S. Boyd, “Semidefinite Programming,” SIAM Review, Vol. 38, No. 1, pp. 49-95, 1996.
    [21] C.-Y. Yeh, W.-p. Su, and S.-J. Lee, “Improving Efficiency of Multi-Kernel Learning for Support Vector Machines,” Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, 2008.
  • 吳志宏 - 召集委員
  • 歐陽振森 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2008-07-21 繳交日期 2008-07-29

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