1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 使用Meta-Learning在蛋白質質譜資料特徵選取之探討
Feature Selection via Meta-Learning on Proteomic Mass Spectrum Data
作者 陳詩佳
貢獻者 郭訓志
陳詩佳
關鍵詞 特徵選取
串聯法
蛋白質質譜
支持向量機
日期 2006
上傳時間 2009-09-14
摘要 癌症高居國人十大死因之首，由於癌症初期病患接受適時治療的存活率較高，因此若能「早期發現，早期診斷，早期治療」則可降低死亡率。本研究主要針對「表面強化雷射解析電離飛行質譜技術」(Surface-Enhanced Laser Desorption / Ionization Time-of-Flight Mass Spectrometry，SELDI-TOF-MS)所蒐集而來的攝護腺癌症蛋白質質譜之事前處理資料進行分析。目的是希望藉由Meta-Learning的方式結合分類器，並以逐步特徵選取之，期望以較少且具代表的特徵變數將資料分類，以達到較高的正確率。本文利用正確率決定逐步特徵選取時變數加入的順序，並進一步以Elastic Net與判定係數作為特徵變數排序依據，以改善變數間共線性高的問題。並且考慮投票法(多數表決法與權重投票法)以及串聯法(cascading)：多個分類器串聯與單一分類器串聯。研究發現，以判定係數刪選特徵變數加入的先後順序並以支持向量機(Support Vector Machine，SVM)串聯的特徵選取結果在各分類下皆有良好表現，為較佳的特徵選取方式。
     
     關鍵字：特徵選取、串聯法、蛋白質質譜、meta-learning、支持向量機
參考文獻 牛頓雜誌編輯部，「孜孜不倦地實驗，也會找到新發現；訪問日本島津製
作所田中耕一研究員」，牛頓雜誌國際中文版第235期，2003年3月號。
牛頓雜誌編輯部，「我的新挑戰！訪問日本島津製作所田中耕一紀念質量
分析研究所」，牛頓雜誌國際中文版第242期，2003年10月號。
行政院衛生署，「中華民國九十四年台灣地區死因統計結果摘要」。
網址：http://www.doh.gov.tw/statistic/data/死因摘要/94年/94.htm
行政院衛生署，國民健康局，「94年度衛生教育宣導主軸－癌症預防」。
網址：http://www.bhp.doh.gov.tw/BHP/index.jsp
行政院衛生署，「中華民國九十四年臺灣地區主要癌症死亡原因」。
網址：http://www.doh.gov.tw/statistic/data/死因摘要/94年/表8.xls
全國癌症病友服務中心，「攝護腺癌(90.02.01)衛教手冊之十八」。
網址：http://www2.cch.org.tw/OURHOME/booklet/booklet18.htm
徐竣建，「重疊法應用於蛋白質質譜資料」，國立政治大學統計系研究所碩士論文，2006年，指導教授：余清祥博士。
國泰綜合醫院，癌症資訊網，「攝護腺癌症簡介」。
網址：http://www1.cgh.org.tw/content/healthy/cancerx/newpage19.htm
黃仁澤，「對於高維度資料進行特徵選取─應用於分類蛋白質質譜儀資料」，國立政治大學統計系研究所碩士論文，2005年，指導教授：郭訓志博士、薛慧敏博士。
葉勝宗，「使用AUC特徵選取法在蛋白質質譜資料分析之應用」，國立政治大學統計系研究所碩士論文，2006年，指導教授：張源俊博士，郭訓志博士。
陳敏鋑，「認識癌症」，癌症關懷季刊，德桃基金會。
網址：http://med.mc.ntu.edu.tw/~onc/Lecture/cancer1.html
賴基銘，「癌症篩檢未來的展望：SELDI血清蛋白指紋圖譜的應用」，國家
衛生研究院電子報第52期，2004年6月25日。
Adam, B.L., Qu, Y., Davis, J.W., Ward, M.D., Clements, M.A., Cazares, L.H.,
Semmes, O.J., Schellhammer, P.F., Yasui, Y., Feng, Z. and Wright, G.L. Jr.
(2002) “Serum Protein Fingerprinting Coupled with a Pattern- matching
Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia
and Healthy Men.” Cancer Research, Vol. 62, No. 13, pp. 3609-14.
Alpaydin, E. and Kaynak, C. (1998), “Cascading Classifiers.” Kybernetika, Vol. 34, No. 4, pp. 369-374.
Alpaydin, E. and Kaynak, C. (2000) “MultiStage Cascading of Multiple Classifiers: One Man’s Noise is Another Man’s Data.” In Seventeenth International Conference on Machine Learning, ed. P. Langley, pp. 455-462. San Francisco: Morgan Kaufmann.
Alpaydin, E. (2004), Introduction to Machine Learning, MIT Press.
Bryan,J. G. (1951), “The Generalized Discriminant Function: Mathematical
Foundations and Computational Routine.” Harvard Educational Review,
Vol. 21, pp. 90-95.
Breiman, L. (1996) “Bagging Predictor.” Machine Learning, Vol. 24, No. 2, pp.123-140.
Burbidge, R., Trotter, M., Buxton, B. F. and Holden, S. B. (2001), “Drug Design by Machine Learning: Support Vector Machine for Pharmaceutical Data Analysis.” Computers and Chemistry, Vol. 26, pp. 5-14.
Chang, Y. C. and Lin, S. C. (2004), “Synergy of Logistic Regression and Support Vector Machine in Multiple-Class Classification.” LNCS, Vol. 3177, pp.132-141.
Chen, G., Gharib, T. G., Huang, C. C., Thomas, D. G., Shedden, K. A., Taylor, Jeremy M. G., Kardia, Sharon L.R., Misek, D. E., Giordano, T. J., Tannettoni, M. D., Orringer, M.B., Hanash, S. M. and Beer, D. G.. (2002) “Proteomic Analysis of Lung Adenocarcinoma: Identification of a Highly Expressed Set of Proteins in Tumors.” Clinical Cancer Research, Vol. 8, pp. 2298-2305.
Draper, N. R. and Smith, H. (1981), Applied Regression Analysis, 2nd Edn. Wiley, New York.
Dudani, S. A. (1976) “The distance-weighted k-nearest-neighbor rule.”
IEEE Transactions on Systems, Man, and Cybernetics, 6(4):325-327.
Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomic
Problems.” Annals of Eugenics, Vol. 7, pp. 179-188.
Fix, E. and Hodges, J. L. (1951), “Discriminatory Analysis-Nonparametric
Discrimination: Consistency Properties.” Report No. 4, US Air Force School of Aviation Medicine, Random Field, Texas. [Published in Agrawala (1997),
Silverman and Jones (1989) and Dasarathy (1991).]
Furey, T., Schummer, M., Duffy, N., Bednarski, D., Haussler, D. and Cristiannini, N.
(2000), “Support Vector Machine Classification and Validation of Caner Tissue Samples Using Microarray Expression Data.” Bioinformatics, Vol. 16, pp. 906-914.
Guyon, I., Weston, J. and Barnhill, S. “Gene selection for cancer classification using support vector machines.” Machine Learning, 46(1): 389-422
Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning. Springer.
Holland, J.H. (1994) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial
Intelligence, 3rd edn. Cambridge, MA: MIT Press.
Johnson, R. A. and Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
Kohonen, Y. (1982) “Self-Organizing Formation of Topologically Correct Feature Maps.” Biological Cybernetics, Vol. 43, pp. 59-69.
Kohonen, T. (1990) “The Self-Organizing Map”, Proc Inst Electrical Electronics Eng, Vol. 78, pp. 1464-1480.
Lilien, R.H., Farid, H. and Donald, B.R. (2003), “Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum.” Journal of Computational Biology, Vol. 10, No. 6, pp.925-946.
Osuna, E., Freund, R. and Girosi, F. (1997), “Training Support Vector Machines: An Application to Face Detection.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 130-136.
Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C. and Liotta, L.A. (2002) “Use of Proteomic Patterns in Serum to Identify Ovarian Cancer.” Lancet, Vol. 359, Iss. 9306, pp. 572-577.
Qu, Y., Adam, B.L., Thornquist, M., Potter, J.D., Thompson, M.L., Yasui, Y., Davis, J., Schellhammer,P. F., Cazares,L., Clements,M.A., Wright, Jr.G.L. and Feng, Z. (2003), “Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data.” Biometrics, Vol. 59, pp, 143–151.
Rao, C. R. (1948), “The Utilization of Multiple Measurements in Problems of
Model Uncertainty in Generalized Linear Models.” Journal of The Royal Statistical Society series B, Vol. 10, pp. 159-203.
Ripley, B. D. (1996), Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press.
Sauve, A. C. and Speed, T. P. (2004) “ Normalization, Baseline Correction and
Alignment of High-Throughput Mass Spectrometry Data.” Proceedings
Gensips 2004
Schölkopf, B. Herbrich, R. and Smola, A. J. (2001) “The General Representer Theorem.” LNAI, Vol. 2111, pp. 416-426.
Tong, S. and Koller, D. (2002), “Support vector machine active learning with
applications to text classification.” The Journal of Machine Learning Research, Vol.2, pp.45-66.
Trafalis, T. B. and Ince, H. (2000), “Support Vector Machine for Regression
and Application to Financial Forecasting.” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Vol. 6, pp.6348-6353.
Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer, New York.
Wolpert, D. H. (1992), “Stacked Generalization.” Neural Networks, Vol.5, pp241-259.
Wu, B., Abbott, T., Fishman, D., McMurray W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. (2003), “Comparison of Statistical Methods for Classification of Ovarian Cancer Using Mass Spectrometry Data.” Bioinformatics, Vol. 19, No. 13, pp. 1636-1643.
Zhang, X., Mesirov, J. P. and Waltz, D. L. (1992) “Hybird System for Protein Secondary Structure Prediction.” NCBI, Vol. 255, No.4, pp.1049-1063.
Zou, H. and Hastie, T. (2004) “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society, Series B, Vol. 67, pp. 301-320.
描述 碩士
國立政治大學
統計研究所
94354014
95
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094354014
資料類型 thesis
dc.contributor.advisor 郭訓志zh_TW
dc.contributor.author (Authors) 陳詩佳zh_TW
dc.creator (作者) 陳詩佳zh_TW
dc.date (日期) 2006en_US
dc.date.accessioned 2009-09-14-
dc.date.available 2009-09-14-
dc.date.issued (上傳時間) 2009-09-14-
dc.identifier (Other Identifiers) G0094354014en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30917-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 94354014zh_TW
dc.description (描述) 95zh_TW
dc.description.abstract (摘要) 癌症高居國人十大死因之首，由於癌症初期病患接受適時治療的存活率較高，因此若能「早期發現，早期診斷，早期治療」則可降低死亡率。本研究主要針對「表面強化雷射解析電離飛行質譜技術」(Surface-Enhanced Laser Desorption / Ionization Time-of-Flight Mass Spectrometry，SELDI-TOF-MS)所蒐集而來的攝護腺癌症蛋白質質譜之事前處理資料進行分析。目的是希望藉由Meta-Learning的方式結合分類器，並以逐步特徵選取之，期望以較少且具代表的特徵變數將資料分類，以達到較高的正確率。本文利用正確率決定逐步特徵選取時變數加入的順序，並進一步以Elastic Net與判定係數作為特徵變數排序依據，以改善變數間共線性高的問題。並且考慮投票法(多數表決法與權重投票法)以及串聯法(cascading)：多個分類器串聯與單一分類器串聯。研究發現，以判定係數刪選特徵變數加入的先後順序並以支持向量機(Support Vector Machine，SVM)串聯的特徵選取結果在各分類下皆有良好表現，為較佳的特徵選取方式。
     
     關鍵字：特徵選取、串聯法、蛋白質質譜、meta-learning、支持向量機		zh_TW
dc.description.tableofcontents 第壹章 緒論 4
     第一節 研究背景 4
     第二節 研究動機與目的 6
     第三節 研究架構 6
     第貳章 蛋白質質譜資料 8
     第一節 表面強化雷射解析電離飛行質譜技術 8
     第二節 攝護腺癌症蛋白質質譜資料 9
     第三節 蛋白質質譜資料之探討 11
     第參章 文獻探討 12
     第肆章 研究方法 15
     第一節 分類器的介紹 16
     4.1.1 LDA 16
     4.1.2 KNN 18
     4.1.3 SVM 21
     第二節 結合多個分類器之特徵選取 25
     4.2.1 Stacking 26
     4.2.2 Cascading 28
     第三節 特徵選取 30
     第伍章 實證分析 31
     第一節 投票法 33
     5.1.1 多數表決法 33
     5.1.2 權重投票法 36
     第二節 CASCADING 37
     5.2.1 多個分類器之串聯 38
     5.2.2 單一分類器之串聯 42
     第三節 特徵選取之改良 45
     5.3.1 Elastic Net + 單一分類器之串聯 46
     5.3.3 判定係數粹取法 49
     第陸章 結論與建議 52
     參考文獻 54
     附 錄 59		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094354014en_US
dc.subject (關鍵詞) 特徵選取zh_TW
dc.subject (關鍵詞) 串聯法zh_TW
dc.subject (關鍵詞) 蛋白質質譜zh_TW
dc.subject (關鍵詞) 支持向量機zh_TW
dc.title (題名) 使用Meta-Learning在蛋白質質譜資料特徵選取之探討zh_TW
dc.title (題名) Feature Selection via Meta-Learning on Proteomic Mass Spectrum Dataen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 牛頓雜誌編輯部，「孜孜不倦地實驗，也會找到新發現；訪問日本島津製zh_TW
dc.relation.reference (參考文獻) 作所田中耕一研究員」，牛頓雜誌國際中文版第235期，2003年3月號。zh_TW
dc.relation.reference (參考文獻) 牛頓雜誌編輯部，「我的新挑戰！訪問日本島津製作所田中耕一紀念質量zh_TW
dc.relation.reference (參考文獻) 分析研究所」，牛頓雜誌國際中文版第242期，2003年10月號。zh_TW
dc.relation.reference (參考文獻) 行政院衛生署，「中華民國九十四年台灣地區死因統計結果摘要」。zh_TW
dc.relation.reference (參考文獻) 網址：http://www.doh.gov.tw/statistic/data/死因摘要/94年/94.htmzh_TW
dc.relation.reference (參考文獻) 行政院衛生署，國民健康局，「94年度衛生教育宣導主軸－癌症預防」。zh_TW
dc.relation.reference (參考文獻) 網址：http://www.bhp.doh.gov.tw/BHP/index.jspzh_TW
dc.relation.reference (參考文獻) 行政院衛生署，「中華民國九十四年臺灣地區主要癌症死亡原因」。zh_TW
dc.relation.reference (參考文獻) 網址：http://www.doh.gov.tw/statistic/data/死因摘要/94年/表8.xlszh_TW
dc.relation.reference (參考文獻) 全國癌症病友服務中心，「攝護腺癌(90.02.01)衛教手冊之十八」。zh_TW
dc.relation.reference (參考文獻) 網址：http://www2.cch.org.tw/OURHOME/booklet/booklet18.htmzh_TW
dc.relation.reference (參考文獻) 徐竣建，「重疊法應用於蛋白質質譜資料」，國立政治大學統計系研究所碩士論文，2006年，指導教授：余清祥博士。zh_TW
dc.relation.reference (參考文獻) 國泰綜合醫院，癌症資訊網，「攝護腺癌症簡介」。zh_TW
dc.relation.reference (參考文獻) 網址：http://www1.cgh.org.tw/content/healthy/cancerx/newpage19.htmzh_TW
dc.relation.reference (參考文獻) 黃仁澤，「對於高維度資料進行特徵選取─應用於分類蛋白質質譜儀資料」，國立政治大學統計系研究所碩士論文，2005年，指導教授：郭訓志博士、薛慧敏博士。zh_TW
dc.relation.reference (參考文獻) 葉勝宗，「使用AUC特徵選取法在蛋白質質譜資料分析之應用」，國立政治大學統計系研究所碩士論文，2006年，指導教授：張源俊博士，郭訓志博士。zh_TW
dc.relation.reference (參考文獻) 陳敏鋑，「認識癌症」，癌症關懷季刊，德桃基金會。zh_TW
dc.relation.reference (參考文獻) 網址：http://med.mc.ntu.edu.tw/~onc/Lecture/cancer1.htmlzh_TW
dc.relation.reference (參考文獻) 賴基銘，「癌症篩檢未來的展望：SELDI血清蛋白指紋圖譜的應用」，國家zh_TW
dc.relation.reference (參考文獻) 衛生研究院電子報第52期，2004年6月25日。zh_TW
dc.relation.reference (參考文獻) Adam, B.L., Qu, Y., Davis, J.W., Ward, M.D., Clements, M.A., Cazares, L.H.,zh_TW
dc.relation.reference (參考文獻) Semmes, O.J., Schellhammer, P.F., Yasui, Y., Feng, Z. and Wright, G.L. Jr.zh_TW
dc.relation.reference (參考文獻) (2002) “Serum Protein Fingerprinting Coupled with a Pattern- matchingzh_TW
dc.relation.reference (參考文獻) Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasiazh_TW
dc.relation.reference (參考文獻) and Healthy Men.” Cancer Research, Vol. 62, No. 13, pp. 3609-14.zh_TW
dc.relation.reference (參考文獻) Alpaydin, E. and Kaynak, C. (1998), “Cascading Classifiers.” Kybernetika, Vol. 34, No. 4, pp. 369-374.zh_TW
dc.relation.reference (參考文獻) Alpaydin, E. and Kaynak, C. (2000) “MultiStage Cascading of Multiple Classifiers: One Man’s Noise is Another Man’s Data.” In Seventeenth International Conference on Machine Learning, ed. P. Langley, pp. 455-462. San Francisco: Morgan Kaufmann.zh_TW
dc.relation.reference (參考文獻) Alpaydin, E. (2004), Introduction to Machine Learning, MIT Press.zh_TW
dc.relation.reference (參考文獻) Bryan,J. G. (1951), “The Generalized Discriminant Function: Mathematicalzh_TW
dc.relation.reference (參考文獻) Foundations and Computational Routine.” Harvard Educational Review,zh_TW
dc.relation.reference (參考文獻) Vol. 21, pp. 90-95.zh_TW
dc.relation.reference (參考文獻) Breiman, L. (1996) “Bagging Predictor.” Machine Learning, Vol. 24, No. 2, pp.123-140.zh_TW
dc.relation.reference (參考文獻) Burbidge, R., Trotter, M., Buxton, B. F. and Holden, S. B. (2001), “Drug Design by Machine Learning: Support Vector Machine for Pharmaceutical Data Analysis.” Computers and Chemistry, Vol. 26, pp. 5-14.zh_TW
dc.relation.reference (參考文獻) Chang, Y. C. and Lin, S. C. (2004), “Synergy of Logistic Regression and Support Vector Machine in Multiple-Class Classification.” LNCS, Vol. 3177, pp.132-141.zh_TW
dc.relation.reference (參考文獻) Chen, G., Gharib, T. G., Huang, C. C., Thomas, D. G., Shedden, K. A., Taylor, Jeremy M. G., Kardia, Sharon L.R., Misek, D. E., Giordano, T. J., Tannettoni, M. D., Orringer, M.B., Hanash, S. M. and Beer, D. G.. (2002) “Proteomic Analysis of Lung Adenocarcinoma: Identification of a Highly Expressed Set of Proteins in Tumors.” Clinical Cancer Research, Vol. 8, pp. 2298-2305.zh_TW
dc.relation.reference (參考文獻) Draper, N. R. and Smith, H. (1981), Applied Regression Analysis, 2nd Edn. Wiley, New York.zh_TW
dc.relation.reference (參考文獻) Dudani, S. A. (1976) “The distance-weighted k-nearest-neighbor rule.”zh_TW
dc.relation.reference (參考文獻) IEEE Transactions on Systems, Man, and Cybernetics, 6(4):325-327.zh_TW
dc.relation.reference (參考文獻) Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomiczh_TW
dc.relation.reference (參考文獻) Problems.” Annals of Eugenics, Vol. 7, pp. 179-188.zh_TW
dc.relation.reference (參考文獻) Fix, E. and Hodges, J. L. (1951), “Discriminatory Analysis-Nonparametriczh_TW
dc.relation.reference (參考文獻) Discrimination: Consistency Properties.” Report No. 4, US Air Force School of Aviation Medicine, Random Field, Texas. [Published in Agrawala (1997),zh_TW
dc.relation.reference (參考文獻) Silverman and Jones (1989) and Dasarathy (1991).]zh_TW
dc.relation.reference (參考文獻) Furey, T., Schummer, M., Duffy, N., Bednarski, D., Haussler, D. and Cristiannini, N.zh_TW
dc.relation.reference (參考文獻) (2000), “Support Vector Machine Classification and Validation of Caner Tissue Samples Using Microarray Expression Data.” Bioinformatics, Vol. 16, pp. 906-914.zh_TW
dc.relation.reference (參考文獻) Guyon, I., Weston, J. and Barnhill, S. “Gene selection for cancer classification using support vector machines.” Machine Learning, 46(1): 389-422zh_TW
dc.relation.reference (參考文獻) Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning. Springer.zh_TW
dc.relation.reference (參考文獻) Holland, J.H. (1994) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificialzh_TW
dc.relation.reference (參考文獻) Intelligence, 3rd edn. Cambridge, MA: MIT Press.zh_TW
dc.relation.reference (參考文獻) Johnson, R. A. and Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, Prentice-Hall, Inc. Upper Saddle River, NJ, USA.zh_TW
dc.relation.reference (參考文獻) Kohonen, Y. (1982) “Self-Organizing Formation of Topologically Correct Feature Maps.” Biological Cybernetics, Vol. 43, pp. 59-69.zh_TW
dc.relation.reference (參考文獻) Kohonen, T. (1990) “The Self-Organizing Map”, Proc Inst Electrical Electronics Eng, Vol. 78, pp. 1464-1480.zh_TW
dc.relation.reference (參考文獻) Lilien, R.H., Farid, H. and Donald, B.R. (2003), “Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum.” Journal of Computational Biology, Vol. 10, No. 6, pp.925-946.zh_TW
dc.relation.reference (參考文獻) Osuna, E., Freund, R. and Girosi, F. (1997), “Training Support Vector Machines: An Application to Face Detection.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 130-136.zh_TW
dc.relation.reference (參考文獻) Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C. and Liotta, L.A. (2002) “Use of Proteomic Patterns in Serum to Identify Ovarian Cancer.” Lancet, Vol. 359, Iss. 9306, pp. 572-577.zh_TW
dc.relation.reference (參考文獻) Qu, Y., Adam, B.L., Thornquist, M., Potter, J.D., Thompson, M.L., Yasui, Y., Davis, J., Schellhammer,P. F., Cazares,L., Clements,M.A., Wright, Jr.G.L. and Feng, Z. (2003), “Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data.” Biometrics, Vol. 59, pp, 143–151.zh_TW
dc.relation.reference (參考文獻) Rao, C. R. (1948), “The Utilization of Multiple Measurements in Problems ofzh_TW
dc.relation.reference (參考文獻) Model Uncertainty in Generalized Linear Models.” Journal of The Royal Statistical Society series B, Vol. 10, pp. 159-203.zh_TW
dc.relation.reference (參考文獻) Ripley, B. D. (1996), Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press.zh_TW
dc.relation.reference (參考文獻) Sauve, A. C. and Speed, T. P. (2004) “ Normalization, Baseline Correction andzh_TW
dc.relation.reference (參考文獻) Alignment of High-Throughput Mass Spectrometry Data.” Proceedingszh_TW
dc.relation.reference (參考文獻) Gensips 2004zh_TW
dc.relation.reference (參考文獻) Schölkopf, B. Herbrich, R. and Smola, A. J. (2001) “The General Representer Theorem.” LNAI, Vol. 2111, pp. 416-426.zh_TW
dc.relation.reference (參考文獻) Tong, S. and Koller, D. (2002), “Support vector machine active learning withzh_TW
dc.relation.reference (參考文獻) applications to text classification.” The Journal of Machine Learning Research, Vol.2, pp.45-66.zh_TW
dc.relation.reference (參考文獻) Trafalis, T. B. and Ince, H. (2000), “Support Vector Machine for Regressionzh_TW
dc.relation.reference (參考文獻) and Application to Financial Forecasting.” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Vol. 6, pp.6348-6353.zh_TW
dc.relation.reference (參考文獻) Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer, New York.zh_TW
dc.relation.reference (參考文獻) Wolpert, D. H. (1992), “Stacked Generalization.” Neural Networks, Vol.5, pp241-259.zh_TW
dc.relation.reference (參考文獻) Wu, B., Abbott, T., Fishman, D., McMurray W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. (2003), “Comparison of Statistical Methods for Classification of Ovarian Cancer Using Mass Spectrometry Data.” Bioinformatics, Vol. 19, No. 13, pp. 1636-1643.zh_TW
dc.relation.reference (參考文獻) Zhang, X., Mesirov, J. P. and Waltz, D. L. (1992) “Hybird System for Protein Secondary Structure Prediction.” NCBI, Vol. 255, No.4, pp.1049-1063.zh_TW
dc.relation.reference (參考文獻) Zou, H. and Hastie, T. (2004) “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society, Series B, Vol. 67, pp. 301-320.zh_TW