一、中文部份
1.江世傑，模糊類神經網路在消費性貸款之應用，國立成功大學工業管理研究所碩士論文，2000年。
2.李天行、唐筱菁，「整合傳統財務指標與智慧資本以建構企業危機預警系統─類神經網路與MARS之應用」，資訊管理學報，11(2)，2004年，頁161-190。
3.李美笑，信用卡持卡人信用風險之研究，逢甲大學保險學系研究所碩士論文，2002年。
4.何貴清，消費者小額信用貸款之信用風險研究-以一商業銀行客戶為例，中山大學人力資源管理研究所碩士論文，2002年。
5.邱志洲、李天行、周宇超、呂奇傑，「整合鑑別分析與類神經網路在資料探勘上之應用」，工業工程學刊，19(2)，2002年，頁9-22。
6.林其鴻，資料探勘於財務時間序列預測模式之建構-以日經225期貨與現貨指數為例，輔仁大學金融研究所，2005年。
7.林勉今，消費性貸款授信風險評估之研究-以X銀行為例，大同大學事業經營研究所碩士在職專班碩士論，2004年。
8.林寶安，「臺灣費金融的演變及其社會經濟意義」，臺灣社會學刊，27，2002年，頁107-162。
9.洪仁杰、莊維瑩及秦榮志，「地區金融機構授信決策之研究-以高屏地區為例」，台灣土地金融季刋，37(1)，2000年。
10.馬芳資，信用卡信用風險預警範例學習系統之研究，國立政治大學資訊管理研究所碩士論文，1994年。
11.楊世彬，「信用風險管理架構簡介」，華南金控，39，2006年3月，頁13-18。

12.紀明宏，適用於高維度資料分類之階層式辨識系統，國立台中教育大學教育測驗統計研究所理學碩士論文，2007年。
13.陳怡妃、李天行 「資料探勘顧客保留分類模式之建構-以健康休閒俱樂部為例」, 2003客服產業學術論文獎頒獎暨客服產業學術研討會, 中華電信電信研究所, 台北, 2003年。
14.陳宗豪，消費者小額信用貸款之信用風險研究-甄選的觀點，國立中山大學人力資源管理研究所碩士論文，2000年。
15.陳炳良，「信用卡的風險管理」，金融財務季刋，3，1999年。
16.陳敬聰，信用卡信用風險評估之研究，國立雲林技術學院資訊管理技術研究所碩士論文，1996年。
17.陳鴻文，個人小額信用貸款授信模式之個案研究，國立高雄第一科技大學財務管理研究所碩士論文，2002年。
18.陳慧瀅，臺灣地區汽車市場需求預測之實證：無母數雲狀迴歸加法性模型之應用，國立中央大學產業經濟研究所碩士論文，1994年。
19.郭伯臣，「統計樣式辨認於測驗資料之應用」，測驗統計年刊，13(下)，2005年，頁146-170。
20.郭敏華，債信評等，臺北:智勝，2000年。
21.張淑珍，利用一次性的SQL改良決策樹建立信用卡審核之信用評等，東吳大學資訊科學系碩士論文，2005年。
22.彭慧雯，建構信用卡資料挖礦架構及其實證研究，國立臺北科技大學生產系統工程與管理研究所碩士論文，2001年。
23.葉秋南，美國金融業風險管理，台北：財團法人金融人員研究訓練中心，1998年。
24.賴靜慧、陳怡妃、李天行，「多元適應性雲形迴歸於資料探勘之應用」，德明學報，2003年，頁71-87
25.簡安泰，消費者信用評分制度之研究，國立政治大學企業管理研究所碩士論文，1971年。
26.龔昶元，「Logistic Regression 模式應用於信用卡信用風險審核之研究-以國內某銀行信用卡中心為例」，臺北銀行月刊，28(9)，1998年，頁35-49。
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9.Cherkassky, V. and Ma, Y., “Practical Selection of SVM Parameters and Noise Estimation for SVM Regression”, Neural Networks, 17, 2004, pp. 113-126.
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12.Churchill, G. A., Nevin, J. R. and Watson, R. R., “The Role of Credit Scoring in the Loan Decision”, Credit World, 3, March, 1977, pp. 6-10.
13.Cooper, D. R. and Emory, C. W., Business Research Method, Chicago: Irwin, 1998.
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17.Craven, P. and Wahba, G., “Smoothing Noisy Data with Spline Functions, Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-validation”, Numberische Mathematik, 31, 1979, pp. 317-403.
18.Cristianini, N. and Shawe-Tayor, J., An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.
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27.Fish, K. E., Barnes, J. H. and Aiken, M. W., “Artificial Neural Networks: a New Methodology for Industrial Market Segmentation”, Industrial Marketing Management, 24, 1995, pp. 431-438.


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30.Friedman, J. H. and Roosen, C. B., “An Introduction to Multivariate Adaptive Regression Splines”, Statistical Methods in Medical Research, 4, 1995, pp. 197-217.
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32.Fukunage, K., Introduction to Statistical Pattern Recognition, San Diego, CA: Academic, 1990.
33.Gately, E., Neural Networks for Financial Forecasting, John Wiley and Sons, 1996.
34.Gothe, P., “Credit Bureau Point Scoring Sheds Light on Shades of Gray”, the Credit World, June, 1990, pp. 26-29.
35.Griffin, W. L., Fisher, N. I., Friedman, J. H. and Ryan, C. G., “Statistical Techniques for the Classification of Chromites in Diamond
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36.Gunn, S. R., “Support Vector Machines for Classification and Regression,” Technical Report, Department of Electronics and Computer Science, University of Southampton, 1998.
37.Hsieh, N. C., “An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers”, Expert Systems with Applications, 27, 2004, pp. 623-633.
38.Hsieh, N. C., “Hybrid Mining Approach in the Design of Credit Scoring Models”, Expert Systems with Applications, 28(4), 2005, pp. 655-665.
39.Hughes, G. F., “On the Mean Accuracy of Statistical Pattern Recognition”, IEEE Transaction Information Theory, IT-14, January, 1968, pp. 55- 63.
40.Huang, W., Nakamori, Y. and Wang, S. Y., “Forecasting Stock Market Movement Direction with Support Vector Machine”, Computers and Operations Research, 32(10), 2005, pp. 2513-2522.
41.Huang, Z., Chen, H., Hsu, C. J., Chen, W. H. and Wu, S., “Credit Rating Analysis with Support Vector Machines and Neural Networks: a Market Comparative Study”, Decision Support Systems, 37(4), 2004, pp. 543-558.
42.Jain, A. K., Mao, J. and Mohiuddin, K., “Artificial Neural Network: a Tutorial”, IEEE Computer, 29, 1996, pp. 31-44.
43.Johnson, R. A. and Wichern, D. W., Applied Multivariate Statistical Analysis (5th ed.), Upper Saddle River, NJ: Prentice-Hall, 2002.
44.Kim, K. J., “Financial Time Series Forecasting Using Support Vector Machines”, Neurocomputing, 55, 2003, pp. 307-319.
45.Kuo, B. C., Chang, C. W., Hung, C. C. and Wang, H. P., “A Comparison of Hierarchical Classification Processes Based on Hyperspetcral Image”, IEEE International Geoscience and Remote Sensing Symposium, Denver, Colorado, 2006.
46.Kuo, B. C. and Chang, K. Y., “Feature Extractions for Small Sample Size Classification Problem”, IEEE Transaction on Geoscience and Remote Sensing, 45 (3), March, 2007, pp.756-764.


47.Kuo, B.C. and Landgrebe, D. A., “Nonparametric Weighted Feature Extraction for Classification”, IEEE Transaction on Geoscience and Remote Sensing, 42 (5), May, 2004, pp. 1096-1105.
48.Landgrebe, D. A., Signal Theory Methods in Multispectral Remote Sensing, New York: Wiley, 2003.
49.Lee, G., Sung, T. K. and Chang, N., “Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction”, Journal of Management Information Systems, 16(1), 1999, pp. 63-85.
50.Lee, T. S. and Chen, I. F., “A Two-stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines”, Expert Systems with Applications, 28(4), 2005, pp. 743–752.
51.Lee, T. S., Chiu, C. C., Lu C. J. and Chen, I. F., “Credit Scoring Using Hybrid Neural Discriminant Technique”, Expert Systems with Applications, 23(3), 2002, pp. 245-254.
52.Lewis, P. A. W. and Stevens, J. G., “Nonlinear Modeling of Time Series Using Multivariate Adaptive Regression Splines (MARS),” Journal of American Statistical Association, 86(416), 1991, pp. 864-877.
53.Li, S., Kwok, J.T., Zhu, H. and Wang, Y., “Texture Classification Using the Support Vector Machines”, Pattern Recognition, 36(12), 2003, pp. 2883-2893.
54.Li, S. T., Shiue, W. and Huang, M. H., “The Evaluation of Consumer Loans Using Support Vector Machines”, Expert Systems with Applications, 30, 2006, pp. 772-782.
55.Lim, M. K. and Sohn, S. Y., “Cluster-based Dynamic Scoring Model”, Expert Systems with Applications, 32, 2007, pp. 427-431.


56.Min, J. H. and Lee, Y. C., “Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters”, Expert Systems with Applications, 28(4), 2005, pp. 603-614.
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62. Rock, A., “Sure Ways to Score with Lender”, Money, September, 1984, pp. 55.
63.Rothstein, H. G., “The Effects of Time Pressure on Judgment in Multiple Cue Probability Learning”, Organizational Behavior and Human Decision Process, 73(1), February, 1986, pp. 83-92.
64.Rumelhart, D. E., Hinton, D. E. and Williams, R. J., “Learning Internal Representations by Error Propagation in Parallel Distributed Processing”, MIT Press, 1, 1986, pp. 318-362.

65.Salford System, MARS V2.0-for Windows 95/98/NT, San Diego, CA: Salford System, 2001.
66.Sanchez, M. S. and Saeabia, L. A., “Efficiency of Multi-layered Feed-forward Neural Networks on Classification in Relation to Linear
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84.Vapnik V. N., Golowich, S. and Smola, A. J., “Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing”, Advances in Neural Information Processing Systems, 9, 1997, pp. 281-287.
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三、網站資料部份
1.中央銀行，消費性貸款，檢索日期 2006年12月8日，網址：http://www.cbc.gov.tw/.
2.行政院銀行局，歷年逾放比率，檢索日期 2006年12月10日，網址： http://www.banking.gov.tw/dp.asp?mp=7.
3.Chang, C. C. and Lin, C.J., LIBSVM: a library for support vector machines software, 2004, available for download from http://www.csie.ntu.edu.tw/ ~cjlin/libsvm.

4.Duin, R, P. W., PRTools, a matlab toolbox for pattern recognition, 2002, available for download from http://www.ph.tn.tudelft.nl/prtools/.
5.Hsu, C. W., Lin, C. C. and Lin, C. J., “A practical guide to supportvector classification”, 2003, retrieved Nov. 21, 2006, from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
6.Landgrebe, D. and Biehl, L., Multispec: A freeware multispectral image data analysis system, 2007, available for download from http://www.ece.purdue.edu/~biehl/MultiSpec/.