參考文獻 |
中文部分
〔1〕 李浩平,「運用NGD建立適用於使用者回饋資訊不足之文件過濾系統」,國立中央大學,碩士論文, 民國100年。
〔2〕 鄭奕駿,「離線搜尋Wikipedia以縮減NGD運算時間之研究」,國立中央大學,碩士論文, 民國101年。
英文部分
〔3〕 Boutell, M. R., Luo, J., Shen, X., and Brown, C. M., "Learning multi-label scene classification", Pattern recognition, vol. 37, pp. 1757-1771, 2004.
〔4〕 Brandes, U., "A faster algorithm for betweenness centrality", Journal of Mathematical Sociology, vol. 25, pp. 163-177, 2001.
〔5〕 Chang, H.-C. and Chiun-Chieh, H., "Using topic keyword clusters for automatic document clustering", IEICE TRANSACTIONS on Information and Systems, vol. 88, pp. 1852-1860, 2005.
〔6〕 Chen, P.-I. and Lin, S.-J., "Automatic keyword prediction using Google similarity distance", Expert Systems with Applications, vol. 37, pp. 1928-1938, 2010.
〔7〕 Chen, P.-I. and Lin, S.-J., "Word AdHoc network: using Google core distance to extract the most relevant information", Knowledge-Based Systems, vol. 24, pp. 393-405, 2011.
〔8〕 Cilibrasi, R. L. and Vitanyi, P. M., "The google similarity distance", Knowledge and Data Engineering, IEEE Transactions, vol. 19, pp. 370-383, 2007.
〔9〕 De Bra, P. and Calvi, L., "AHA: a generic adaptive hypermedia system," in Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia, 1998, pp. 5-12.
〔10〕 Diestel, R., "Graph theory. 2005," ed: Springer-Verlag, 2005.
〔11〕 Dijkstra, E. W., "A note on two problems in connexion with graphs", Numerische mathematik, vol. 1, pp. 269-271, 1959.
〔12〕 Diplaris, S., Tsoumakas, G., Mitkas, P. A., and Vlahavas, I., "Protein classification with multiple algorithms," in Advances in Informatics, ed: Springer, 2005, pp. 448-456.
〔13〕 Girvan, M. and Newman, M. E., "Community structure in social and biological networks", Proceedings of the National Academy of Sciences, vol. 99, pp. 7821-7826, 2002.
〔14〕 Hanani, U., Shapira, B., and Shoval, P., "Information filtering: Overview of issues, research and systems", User Modeling and User-Adapted Interaction, vol. 11, pp. 203-259, 2001.
〔15〕 Joachims, T., Text categorization with support vector machines: Learning with many relevant features: Springer, 1998.
〔16〕 Klinkenberg, R. and Joachims, T., "Detecting concept drift with support vector machines," in Proceedings of the Seventeenth International Conference on Machine Learning (ICML), 2000.
〔17〕 Liu, Y.-C., Wang, X.-L., and Liu, B.-Q., "A feature selection algorithm for document clustering based on word co-occurrence frequency," in Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference 2004, pp. 2963-2968.
〔18〕 Magnini, B. and Strapparava, C., "User modelling for news web sites with word sense based techniques", User Modeling and User-Adapted Interaction, vol. 14, pp. 239-257, 2004.
〔19〕 Newman, M. E. and Girvan, M., "Finding and evaluating community structure in networks", Physical review E, vol. 69, p. 026113, 2004.
〔20〕 Page, E., "Continuous inspection schemes", Biometrika, vol. 41, pp. 100-115, 1954.
〔21〕 Quinlan, J. R., "Induction of decision trees", Machine learning, vol. 1, pp. 81-106, 1986.
〔22〕 Razmerita, L., Angehrn, A., and Maedche, A., "Ontology-based user modeling for knowledge management systems," in User Modeling 2003, ed: Springer, 2003, pp. 213-217.
〔23〕 Salton, G. and Buckley, C., "Term-weighting approaches in automatic text retrieval", Information processing & management, vol. 24, pp. 513-523, 1988.
〔24〕 Schwarzkopf, E., Heckmann, D., Dengler, D., and Kröner, A., "Mining the structure of tag spaces for user modeling," in Complete On-Line Proceedings of the Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling. Corfu, Griechenland, 2007, pp. 63-75.
〔25〕 Seidman, S. B., "Network structure and minimum degree", Social networks, vol. 5, pp. 269-287, 1983.
〔26〕 Tsoumakas, G. and Katakis, I., "Multi-label classification: An overview", International Journal of Data Warehousing and Mining (IJDWM), vol. 3, pp. 1-13, 2007.
〔27〕 Tsymbal, A., "The problem of concept drift: definitions and related work", Computer Science Department, Trinity College Dublin, 2004.
〔28〕 Tsymbal, A., Pechenizkiy, M., Cunningham, P., and Puuronen, S., "Dynamic integration of classifiers for handling concept drift", Information Fusion, vol. 9, pp. 56-68, 2008.
〔29〕 Tufis, D. and Mason, O., "Tagging romanian texts: a case study for qtag, a language independent probabilistic tagger," in Proceedings of the First International Conference on Language Resources and Evaluation (LREC), 1998, pp. 589-596.
〔30〕 Vitányi, P. M., Balbach, F. J., Cilibrasi, R. L., and Li, M., "Normalized information distance," in Information theory and statistical learning, ed: Springer, 2009, pp. 45-82.
〔31〕 White, S., O’Madadhain, J., Fisher, D., and Boey, Y.-B., "JUNG: Java Universal Network/Graph Framework", available now at: http://jung.sourceforge.net/index.html, 2004.
〔32〕 Xioufis, E. S., Spiliopoulou, M., Tsoumakas, G., and Vlahavas, I., "Dealing with concept drift and class imbalance in multi-label stream classification," in Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Two, 2011, pp. 1583-1588.
〔33〕 Zhang, P., Zhu, X., and Shi, Y., "Categorizing and mining concept drifting data streams," in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 812-820.
〔34〕 Žliobaitė, I., "Learning under concept drift: an overview", arXiv preprint arXiv:1010.4784, 2010. |