政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/83297
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113160/144130 (79%)
造訪人次 : 50754445      線上人數 : 674
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/83297


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/83297


    題名: 倒傳導神經網路的有效性、使用性與顯著性之研究
    The Study of Validity, Utilization and Salience of the BP Networks
    作者: 陳怡達
    Chen, Yi-Da
    貢獻者: 蔡瑞煌
    Ray Tsaih
    陳怡達
    Chen, Yi-Da
    關鍵詞: 分類學習
    倒傳導神經網路
    敏感度分析
    競爭學習
    遮蔽效應
    不相關線索的影響
    category learning
    back propagation neural networks
    sensitivity analysis
    competitive learning
    overshadowing
    the deleterious of an irrelevant cue
    日期: 2000
    上傳時間: 2016-03-31 15:43:07 (UTC+8)
    摘要: 本研究的主要目的是檢視倒傳導神經網路是否具有人類在分類學習上所呈現出來的學習效應 — 競爭學習、遮蔽效應與不相關線索的影響。在實驗中，我們採用兩種倒傳導神經網路，來測試激發函數是否會影響倒傳導神經網路的學習。此兩種倒傳導神經網路分別採用sigmoid激發函數與hyperbolic-tangent激發函數。實驗結果顯示，以sigmoid為激發函數與以hyperbolic-tangent為激發函數的倒傳導神經網路都具有這三個學習效應。還有，以sigmoid為激發函數的倒傳導神經網路所呈現出來的學習效應比以hyperbolic-tangent為激發函數的倒傳導神經網路來得顯著。本研究的次要目的在於瞭解有效性（使用性）與敏感度分析的數值是否有對應關係。實驗結果顯示，線索A與線索B的敏感度分析數值差異可以反映出線索A與線索B的有效性差異。然而，敏感度分析數值卻無法準確地顯示線索的有效性數值。
    The main objective of this research is to examine whether back propagation neural networks (BP) have the learning effects found in human category learning — competitive learning, overshadowing and the deleterious of an irrelevant cue. Two kinds of BP, BP with sigmoid activation function and BP with hyperbolic-tangent activation function, are investigated to see if the activation function will make BP behave differently. According to the results of our experiments, these three learning effects are demonstrated both in BP with sigmoid and BP with hyperbolic-tangent, but they seems more significant in BP with sigmoid than in BP with hyperbolic-tangent. The second objective of our research is to see if there is a correspondence between the validity (the utilization) and the value of sensitivity analysis, R. From the results of our experiments, we observe that the difference between values of sensitivity analysis with respect to Cue A and Cue B reflects the difference of the validities between Cue A and Cue B. However, the value of sensitivity analysis does not show exactly what validity a cue is.
    參考文獻: 1. Anderson, J.R., Cognitive Psychology and Its Implications, W.H.Freeman, New York, 1990.
    2. Anderson, J.R., Learning and Memory: An Integrated Approach, John Wiley and Sons, New York, 1995.
    3. Boden, M.A., Artificial Intelligence in Psychology: Interdisciplinary Essays, The MIT Press, Cambridge, 1989.
    4. Davey, G., Animal Learning and Conditioning, The Macmillan Press, London, 1981.
    5. Edgell, S.E., ”Configural Information Processing in Two-Cue Probability Learning,” Organizational Behavior and Human Performance, Vol.22, 1978, pp.404-416.
    6. Edgell, S.E. and Roe, R.M., “Dimensional Information Facilitates the Utilization of Configural Information: A Test of the Cestellan-Edgell and the Gluck-Bower Models,” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol.21, 1995, pp.1495-1508.
    7. Gagne, R.M., “Memory Structures and Learning Outcomes,” Review of Educational Research, Vol.48, 1978, pp.178-222.
    8. Gardner, H., The Mind’s New Science: A History of the Cognitive Revolution, Basic Books, New York, 1985.
    9. Gluck, M.A. and Bower, G.H., “From Conditioning to Category Learning: An Adaptive Network Model,” Journal of Experimental Psychology: General, Vol.117, 1988, pp.227-247.
    10. Grossberg, S., Neural Networks and Natural Intelligence, The MIT Press, Cambridge, 1988.
    11. Kruschke J.K. and Johansen M.K., “A Model of Probabilistic Category Learning,” Journal of Experimental Psychology: Learning, Memory and Cognitive, Vol.25, 1999, pp.1083-1119.
    12. Hertz, J., Krogh, A. and Palmer R.G., Introduction to the Theory of Neural Computation, Addison Wesley, New York, 1991.
    13. Medin, D.L. and Edelson, S.M., “Problem Structure and the Use of Base-Rate Information from Experience,” Journal of Experimental Psychology: General, Vol.117, pp.68-85.
    14. Medin, D.L. and Schaffer, M.M., “Context Theory of Classification Learning,” Psychological Review, Vol.85, 1978, pp.207-238.
    15. Morris, R.G.M., Parallel Distributed Processing: Implications for Psychology and Neurobiology, Oxford, New York, 1989.
    16. Nosofsky, R.M., “Similarity, Frequency, and Category Representations,”Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol.14, 1988, pp.54-65.
    17. Regehr, G. and Brooks, L., “Category Organization in Free Classification: The Organizing Effect of an Array of Stimuli,” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol.21, 1995, pp.347-363.
    18. Rescorla, R.A. and Wagner, A.R., "A theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Non-reinforcement," In Black, A.H. and Prokasy, W.F. (Eds.), Classical Conditioning Ⅱ: Current Research and Theory, Appleton Century Crofts, New York, 1972.
    19. Rumelhart, D.E., Hinton, G.E. and Williams, R.J., ”Learning Internal Representations by Error Propagation,” In Rumelhart, D.E. and McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol.1, The MIT Press, Cambridge, 1986, pp.318-362.
    20. Rumelhart, D.E. and McClelland, J.L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol.1, The MIT Press, Cambridge, 1986.
    21. Schalkoff, R.J., Artificial Neural Networks, McGraw-Hill, New York, 1997.
    22. Smith, J.D., Minda, J.P. and Murry, M.J., Jr., “Straight Talk about Linear Separability,” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol.23, 1997, pp.659-680.
    23. Sutton, R.S. and Barto, A.G., “Toward a Modern Theory of Adaptive Networks: Expectation and Prediction,” Psychological Review, Vol.88, 1981, pp. 135-170.
    24. Tsaih, R., “Sensitivity Analysis, Neural Networks, and the Finance,” International Joint Conference on Neural Networks, 1999.
    描述: 碩士
    國立政治大學
    資訊管理學系
    87356017
    資料來源: http://thesis.lib.nccu.edu.tw/record/#A2002002097
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML2238檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    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 ©   - 回饋