政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/35180

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/35180

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113313/144292 (79%)
造访人次 : 50946208 在线人数 : 892

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/35180

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/35180

题名:	The Rule Extraction from Multi-layer Feed-forward Neural Networks
作者:	柯文乾 Ke, Wen-Chyan
贡献者:	蔡瑞煌 Tsahi Ray 柯文乾 Ke, Wen-Chyan
关键词:	知識萃取規則萃取法則萃取債券評價 knowledge extraction rule extraction bond-pricing
日期:	2002
上传时间:	2009-09-18 14:22:32 (UTC+8)
摘要:	神經網路已經被成功地應用於解決各種分類及函數近似的問題，尤其因為神經網路是個萬能的近似器(universal approximator)，所以對於函數近似的問題效果更為顯著。以往對於此類問題雖然多數以線性的分析工具為主，但是實際上多數問題本質上是非線性的，所以對於非線性分析工具的需求其實是很大的。自1986年起，神經網路本身的運作一直被視為一個黑箱作業，難以判斷網路學習結果的合理性，更無法有效地幫助使用者增進其知識，因此提供一套合理及有效的神經網路分析方法是重要。本文提出一套分析神網路系統的方法；利用線性規劃的技巧萃取及分析網路中的規則(rule)，而不需要對任何資料集做分析；進而利用統計無母數方法－符號檢定－歸納出網路中的知識。以債券評價為例，驗證此方法的可行性，實證結果亦顯示此方法所萃取出來的規則是合理的，且由這些萃取出的規則中，所歸納出來有關債券評價的知識多數是合理的。 Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful for function approximation problems because they have been shown to be uni-versal approximators. In the past, for function approximation problems, they were mainly analyzed via tools of linear analyses. However, most of the function approxi-mation problems needed tools of nonlinear analyses in fact. Thus, there is the much demand for tools of nonlinear analyses. Since 1986, the neural network is considered a black box. It is hard to determine if the learning result of a neural network is rea-sonable, and the network can not effectively help users to develop the domain knowl-edge. Thus, it is important to supply a reasonable and effective analytic method of the neural network. Here, we propose an analytic method of the neural network. It can extract rules from the neural network and analyze them via the Linear Programming and does not depend on any data analysis. Then we can generalize domain knowledge from these rules via the sign test, a statistical non-parameter method. We take the bond-pricing as an instance to examine the feasibility of our proposed method. The result shows that these extracted rules are reasonable by our method and that these generalized domain knowledge from these rules is also reasonable.
參考文獻:	Bishop, C. M. (1995). Neural network for pattern recognition. Oxford : Clarendon Press. Fu, L. (1994). Neural networks in computer intelligence. McGraw-Hill, Inc. Gaweda, A. E., Setiono, R., and Zurada, J. M. (2000). "Rule extraction from feed--forward neural network for function approximation." In: Proceedings of the 5th Conference on Neural Networks and Soft Computing, Zakopane, Poland, pp. 311-316. Gill, P. E., Mao, Z. H., and Li, Y. D. (1981). Practical optimization. New York: Aca-demic. Hertz, J., Krogh, A. and Palmer, R. G. (1991). Introduction to the theory of neural computation, Redwood City, CA: Addison Wesley. Hogg, R. V., Tains, E. A. (1997a). Probability and statistical inference-5th ed, New Jersey: Prentice Hall, pp. 394-455. Hogg, R. V., Tains, E. A. (1997b). Probability and statistical inference-5th ed, New Jersey: Prentice Hall, pp. 608-614. Karnin, E. D. (1990). "A simple procedure for pruning back-propagation trained neural networks." IEEE Transactions on Neural Networks, Vol. 1, No. 2, pp.239-242. Kerber, R. (1992). "ChiMerge: Discretization of numeric attributes." In: Proceedings Ninth National Conference on Artificial Intelligence, Menlo Park, CA: AAAI Press, pp. 123-128. Lloyd, S. P. (1982). "Least squares quantization in PCM." IEEE Transactions on In-formation Theory, Vol. 28, No. 2, 129-137. Liu, H., and Setiono, R. (1995). "Chi2: Feature selection and discretization of nu-meric attributes." In: Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, pp. 388-391. Liu, H., and Tan, S. T. (1995). "X2R: A fast rule generator." In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, IEEE Press. Malkiel, B. G., (1962). "Expectations, bond prices, and the term structure of interest rates." Quarterly Journal of Economics, Vol 76, No. 2, pp.197-218. Murty, K. G., (1983). Linear Programming. New York: John Wiley & Sons, pp.91-181. Neter, J., Kuter, M.H., Nachtsheim C.J., and Wasserman W. (1996). Applied linear regression models─3rd ed. Richard D. Irwin, pp. 640. Quinlan, J. R. (1993), C4.5: Programs for machine learning. Sam Mateo, CA: Morgan Kaufmann. Rosenblatt, F. (1958), "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological Review, Vol. 65, pp. 386-408. Rumelhart, D.E., Hinton, G.E., and Williams, R. (1986). "Learning internal repre-sentation by error propagation." Parallel Distributed Processing. Cambridge, MA: MIT Press, Vol. 1, pp. 318-362. Saito, K., and Nakano R. (2002). "Extracting regression rules from neural networks." Neural Network, Vol. 15, No. 10, pp. 1297-1288. Saito, K., and Nakano R. (2000). "Discovery of relevant weight by minimizing cross-validation error." In: Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, pp. 372-375. Seber, G.A.F., and Wild, C.J. (1989). Nonlinear regression. New York: John Wiley & Sons, pp. 465-471 Setiono, R., Leow, W. K., and Zurada, J. M. (2002). "Extraction of rules from artifi-cial neural networks for nonlinear regression," IEEE Transactions on Neural Networks, Vol. 13, No. 3, pp. 564-577. Setiono, R. (1997). "A penalty function approach for pruning feed-forward neural networks." Neural Computation, Vol. 9, No. 1, pp.185-204. Setiono, R., and Liu. H. (1997). "NeuroLinear: From neural networks to oblique de-cision rules." Neurocomputing, Vol. 17, No. 1, pp. 1-24. Setiono, R., and Liu, H. (1996). "Symbolic representation of neural networks." IEEE Computer, Vol. 29,. No. 3, pp. 71-77. Sharpe, W. F. and Alexander, G. J. (1990). Investments-the fourth edition. New Jer-sey: Prentice-Hall, Inc, pp.382-384. Simth, M. (1993). Nerual networks for statistical modeling. New York: Van Nostrand Reinhold, pp.167. Stone, M. (1974). "Cross-validatory choice and assessment of statistical predictions (with discussion)." Journal of the Royal Statistical Society B, Vol. 36, No. 1, pp.111-147. Taha, I. A., and Ghosh, J. (1999). "Symbolic interpretation of artificial neural net-works." IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 3, pp.448-463. Taha, I. A., and Ghosh, J. (1996). "Three techniques for extracting rule from feed-forward networks." In: Dagli, C. H., Akay, M., Fernandez, B., Chen, C. L. P., Ghosh J. (Eds). Intelligent Engineering System Through Artificial Neural Networks (Volume 6), St. Louis: ASME Press, pp.23-28. The MathWorks, Inc. (2002). Optimization Toolbox User’s Guide. [Online]. Avail-able: http://www.mathworks.com/access/helpdesk/help/pdf_doc/optim/optim_tb. pdf Towell, G., and Shavlik, J. (1993). "The extraction of refined rules from knowl-edge-based neural networks." Machine Learning, Vol. 13, No. 1, pp. 71-101. Vapnik, V. (1995). "The nature of statistical learning theory." New York: Springer-Verlag. Van Ooyen, A., Nienhuis, B. (1992). "Improving the convergence of the backpropa-gation algorithm." Neural Networks, Vol. 5, No. 3, pp.465-471. Weijters, T., and Bosch, A. V. D. (1998). "Interpretable neural networks with BP-SOM," In: Tasks and Methods in Applied Artificial Intelligence. Lecture Notes in Artificial Intelligence 1416(A. del Pobil, J. Mira, and M. Ali, eds.), Ber-lin: Springer, pp. 564-573. Zhou, R. R., Chen, S. F., and Chen, Z. Q. (2000). "A statistics based approach for ex-tracting priority rules from trained neural networks." In: Proceedings of the IEEE-INNS-ENNS International Join Conference on Neural Network, Como, It-aly, Vol. 3, pp. 401-406.
描述:	碩士國立政治大學資訊管理研究所 90356002 91
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0090356002
数据类型:	thesis
显示于类别:	[資訊管理學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
35600201.pdf		125Kb	Adobe PDF2	1096	检视/开启
35600202.pdf		86Kb	Adobe PDF2	1105	检视/开启
35600203.pdf		150Kb	Adobe PDF2	1220	检视/开启
35600204.pdf		128Kb	Adobe PDF2	1057	检视/开启
35600205.pdf		208Kb	Adobe PDF2	1306	检视/开启
35600206.pdf		375Kb	Adobe PDF2	1258	检视/开启
35600207.pdf		249Kb	Adobe PDF2	1135	检视/开启
35600208.pdf		297Kb	Adobe PDF2	1081	检视/开启
35600209.pdf		155Kb	Adobe PDF2	1044	检视/开启
35600210.pdf		154Kb	Adobe PDF2	1458	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈