| Reference: | 英文文獻
1. K. Aas and L. Eikvil(1999), Text Categorization: a Survey, Technical Report, no.941, Norwegian Computing Center.
2. Abrahart R. J., See L. & Kneale P. E. (1998). New Tools for Neurobydrologists: Using Network Pruning and Nodel Breeding Algorithms to Discover Optimum Inuts and Architectures. In Proceedings of the 3rd International Conference on Geocomputation. University of Bristol.
3. Ahmad, K., Oliveira, P., Manomaisupat, P., Casey, M. & Taskay, T. (2002). Description of Events: An Analysis of Keywords and Indexical Names, Third International Conference on Language Resources and Evaluation, LREC 2002: Workshop on Event Modelling for Multilingual Document Linking, p29-35.
4. Allan, J., Papka, R. & Lavrenko. V. (1998). On-line New Event Detection and Tracking, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p37-45.
5. Annexstein, F. (2002). Indexing and Representation: The Vector Space Model Retrieved, December 25, 2003, from the World Wide Web: http://www.ececs.uc.edu/~annexste/Courses/cs690/Indexing%20and%20Representation.ppt.
6. Armano G., Marchesi M., & Murru A. (2005). A hybrid genetic-neuralarchitecture forstock indexes forecasting,” Information Sciences, Vol.170, Issue 1, p3-33.
7. Chen, A. S., Leung, M. T., Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research. 30, 6.
8. Dawson C. W. and Wilby R. L. (2001). Hydrological Modeling Using Artificial Neural Networks. Progress in Physical Geography. 25(1), p80-108.
9. Fayyed, U., Piatetsky-Shapiro, G. & Smyth, P. (1996). The KDD Process of Extracting Useful Knowledge from Volumes of Data, Communication of the ACM, Vol.39, p27-34.
10. Ham F. M. & Kostanic I. (2001). Principles of Neurocomputing for Science & Engineering. McGraw-Hill: New York, NY.
11. Hsu, C. W., Chang, C. C., and Lin, C. J. (2010). A Practical Guide to Support Vector Classification http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
12. Hush, D. R. & Horne, B. G. (1993). Progress in supervised neural networks. IEEE Signal Process. Mag. (January 1993), p8-39.
13. Jing, L. P. , Huang, H. K., Shi, H. B. (2002). Improved Feature Selection Approach TFIDF in Text Mining. 1st International Conference on Machine Learning and Cybernetics, Beijing.
14. Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Proceedings of the European Conference on Machine Learning Springer.
15. Han, J. & Kamber, M. (2001). Data Mining: Concepts and Techniuqes, Morgan Kaufmann Publishers, San Francisco, CA.
16. Kim, K. J., Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index.
17. Kurt, H. (2001). On-line new event detection and tracking in a multi-resource environment, MS Thesis, The Institute of Engineering and Science of Bilkent University.
18. Kwok, T.Y. & Yeung, D. Y. (1997). Constructive Algorithms for Struture Learning in Feedforward Neural Networks for Regression Problems. IEEE Transactions on Neural Networks. 3: 630-645.
19. Kwok, T. Y. and Yeung, D. Y. (1997). Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Transcations on Neural Networks. 3: 630-645.
20. Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D., and Allan, J. (2000). Language models for financial news recommendation. In Proceedings of CIKM 2000, p389-396, New York, N.Y., ACM Press.
21. Liu, H. & Motoda, H. (1998). Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic, Norwell, MA, USA.
22. MacQueen, J. (1967). Some Methods for Classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Statist, Prob., 1:281-297.
23. Nguyen, D. & Widrow, B. (1990). Improving the Learning Speed of the 2-Layer Neural Networks by Choosing Initial Values of Adaptive Weights. In Proceedings of the International Joint Conference on Neural Networks. 3. San Diego, CA.
24. Nygren, K. (2004), Stock Prediction – A Neural Network Approach. Master Thesis, Royal Institute of Technology, KTH.
25. Popescu, A. (2001). Implementation of term weighting in a simple IR system, Personal course project, University of Helsinki.
26. Salton, G. (1989). Automatic Text Processing. Addison-Wesley, Reading, Mass.
27. Salton, G. & Gill, M. (1983). Introduction to Modern Information Retrieval, McGraw-Hill.
28. Salton, G., Wong, A. & Yang, C. S. (1975). A Vector Space Model for Automatic Indexing.
29. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), p1-47.
30. Yang, Y., Ault, T., and Pierce, T. (2000). Improving text categorization methods for event tracking , Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
31. Yang, Y. & Pedersen, J. O. (1997). A Comparative Study on Feature Selection in TextCategorization. Proceedings of the Fourteenth International Conference on Machine Learning, p412-420, Nashville, TN, USA.
32. Yang, Y., Pierce, T. & Carbonell, J. (1998). A Study on Retrospective And On-Line Event Detection , Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p28-36.
33. Wu, Y. C. (2008). Predicting the Trend of Taiwan Weighted Stock Index with Text Mining Techniques, NCU IM.
34. Mittermayer, M.-A.(2004). Forecasting Intraday Stock Price Trends with Text Mining Techniques. In: Proceedings 37th Annual Hawaii Int. Conference on System Sciences (HICSS). Big Island, p64.
中文文獻
[1]林章德，2000，上市公司重大投資宣告對股價影響之研究，東海大學管理研究所碩士論文。
[2]林聖哲，2001，針對認購權證建構不同之人工智慧評價，實踐大學企業管理學系研究所碩士論文
[3]李春淋，2010，個股新聞對股價影響之研究-以台股為例，輔仁大學應用統計學系碩士論文。
[4]吳真蕙，2000，專業性報紙頭版新聞對股票價量的影響，中原大學會計系碩士論文。
[5]周宗南、劉瑞鑫，2005，演化式類神經網路應用於台股指數報酬率之預測，財經論文叢刊，第三期，p77-94
[6]胡舜禹，2009，結合PSO及K-Means聚類分析演算法的圖像分割，中山通訊工程研究所碩士在職專班
[7]袁立安，2007，混合式自動文件摘要方法，國立中山大學資訊管理研究所碩士論文
[8]陳稼興、楊孟龍，2000，類神經網路於股市波段預測及選股之應用
[9]章秉純、許清琦, Combining Unsupervised Feature Selection Strategy for Automatic Text Categorization, In Proceedings of the 6th Conference on Artificial Intelligence and Applications, November 9, 2001.
[10]張斐章、張麗秋，2005，類神經網路，台北市：東華書局
[11]黃孝文，2010，雲端運算服務環境下運用文字探勘於語意註解網頁文件分析之研究，國立政治大學資訊管理研究所碩士論文
[12]黃馨瑩、楊建民、李耀中，2009，財經新聞探勘影響股價趨勢之探討-以跨兩岸面板產業為例，
[13]楊踐為、李家豪、類惠貞，2007。應用時間序列分析法建構台灣證券市場之預測交易模型。中華管理評論國際學報，10，3
[14]鍾任明、李維平、吳澤民，2007。運用文字探勘於日內股價漲跌趨勢預測之研究。中華管理評論國際學報，10，1
[15]戴尚學，2003，運用事件偵測與追蹤技術於中文多文件摘要之研究，國立雲林科技大學資訊管理研究所碩士論文
[16]顧皓光，1996，網路文件自動分類，國立台灣大學資訊管理研究所碩士論文
[17]羅華強，2001，類神經網路，台北市：清蔚科技
網站資料
[1]A Tutorial on Clustering Algorithms (2011), 2011年2月3日取自 http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
[2]自由時報電子報。2011年2月1日取自http://www.libertytimes.com.tw/index.htm
[3]中研院CKIP。2011年1月17日取自 http://ckipsvr.iis.sinica.edu.tw
[4]Yahoo API (2011)。2011年1月22日取自http://tw.developer.yahoo.com/cas |
