在機器學習的範疇裡，分類問題(classification)是相當重要的主題之一；支撐向量機(support vector machines, SVM)則是該領域新近十年來蓬勃發展的一項分類技術，且已廣泛地運用在許多領域之上。

　　根據研究，比較多個分類演算法的正確率績效時，支撐向量機的表現相當出色；是一個相當良好的分類工具。然而支撐向量機在運作的過程中，訓練資料全體一致的成本函數(penalty parameter)設定尚有可以改進的空間，本研究的主要工作是在資料前處理(data preprocessing)的階段將訓練資料檔(training set)分析處理，利用權重區分方法給予訓練資料不同的權重(weights)，以加上權重後的訓練資料進行SVM學習，分析比較加入權重區分法後的執行績效；本研究發現當訓練資料集合在樣本數較少的情況時，透過給予訓練資料不同權重的方式降低雜訊資料(noise)的影響可以提升SVM的分類成效。

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中文

王麒瑋,支向機核心函數適用指標之建立,國立成功大學工業管理科學研究所碩士論文,民國92年
王景南,多類支向機之研究,元智大學資訊管理研究所碩士論文,民國92年
韓歆儀,應用兩階段分類法提升SVM法之分類準確率,國立成功大學工業與資訊管理研究所碩士論文，民國93年

英文

Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.

Chang, C.-C., Lin, C.-J. (2001). LIBSVM: A Library for Support Vector Machines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46, 131-159.

Cortes, C. & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.

Friedman, J. (1996). Another approach to polychotomous classification. Tech. Rep., Dept. Statist., Stanford Univ., Stanford, CA.

Holte, R.C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63-91.

Hsu, C.-W. & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, 13, 415-425.

Kim, H. & Park, H. (2004). Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. PROTEINS: Structure, Function, and Bioinformatics, 54, 557-562.

Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In proc. Fourteenth International Joint Confernce on Artificial Intelligence, 1137-1143.

Lin, K. -M. & Lin, C. –J. (2003). A study on reduced support vector machines. IEEE Trans. Neural Networks, 14, 1449-1459.

Meyer, D., Leisch, F.& Hornik, K. (2003). The support vector machine under test. Neurocomputing, 55, 169-186.

Osuna, E., Freund, R., Girosi, F. (1997). Support vector machines: training and applications. Tech. Rep., AI Memo Now 1602. Cambridge, MA: MIT AI Laboratory.

Platt, J.C., Cristianini, N., Shawe-Taylor, J. (2000). Large margin DAG for multiclass classification. Advances in Neural Information Processing Systems (pp.547-553). Cambridge, MA: MIT Press.

Vapnik, N. (1999). The Nature of Statistical Learning Theory. Springer-Verlag. 2nd.

Wang, X. & Zhong, Y. (2003). Statistical learning theory and state of the art in SVM. Proceedings of the Second IEEE International Conference on Cognitive Informatics, 55 – 59.

Witten, I. H. & Frank, E. (2000). Data Mining: pratical machine learning tools and techniques with Java implementations. San Francisco, Morgan Kaufmann.

Zhang, W. & Chen, T. (2003). Classification based on symmetric maximized minimal distance in subspace. Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 100-105.