|
[1]Sakulin, W. (2011). Trademark protection and freedom of expression: an inquiry into the conflict between trademark rights and freedom of expression under European law (Vol. 22). Kluwer Law International. [2]Beebe, B. (2006). An empirical study of the multifactor tests for trademark infringement. Cal. L. Rev., 94, 1581. [3]Trademark Dilution (2018). Retrieved from https://www.inta.org/TrademarkBasics/FactSheets/Pages/Trademark-Dilution-(Intended-for-a-non-legal-audience).aspx [4]Supreme court of United state (2018). Retrieved from https://www.supremecourt.gov/ [5]Legal institute information (2018). Retrieved from https://www.law.cornell.edu/wex/lanham_act [6]Mersky, R. M., & Price, J. (2006). The Dictionary and the Man: The Eighth Edition of Black's Law Dictionary, Edited by Bryan Garner. Washington and Lee Law Review, 63(2), 719. [7]Thomson Corporation (1962). American Jurisprudence § 505.Protecting trademarks, trade names, and service marks—Legal remedies; injunctive relief [8]Roitblat, H. L., Kershaw, A., & Oot, P. (2010). Document categorization in legal electronic discovery: computer classification vs. manual review. Journal of the American Society for Information Science and Technology, 61(1), 70-80. [9]Baron, J. R., & Thompson, P. (2007, June). The search problem posed by large heterogeneous data sets in litigation: possible future approaches to research. In Proceedings of the 11th international conference on Artificial intelligence and law (pp. 141-147). [10]Garner, B. A. (1999). Black’s law dictionary. [11]Ringling Bros.-Barnum & Bailey, Combined Shows, Inc. v. B.E. Windows (1996), 937 F.Supp. 204. [12]Davoodi, E., Kianmehr, K., & Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied intelligence, 39(1), 1-13. [13]Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems , 153-160. [14]Zhou, T., Ren, J., Medo, M., & Zhang, Y. C. (2007). Bipartite network projection and personal recommendation. Physical Review E, 76(4), 046115. [15]Davoodi, E., Kianmehr, K., & Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied intelligence, 39(1), 1-13. [16]Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72. [17]Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence. [18]Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of machine learning research, 2493-2537. [19]Van Meteren, R., & Van Someren, M. (2000, May). Using content-based filtering for recommendation. In Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 47-56. [20]Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999, August). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 230-237). [21]Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence. [22]Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1), 51-89. [23]Collobert, R., & Weston, J. (2008, July). A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning (pp. 160-167). [24]Nallapati, R., Zhou, B., Gulcehre, C., & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023. [25]Jaech, A., Heck, L., & Ostendorf, M. (2016). Domain adaptation of recurrent neural networks for natural language understanding. arXiv preprint arXiv:1604.00117. [26]Nallapati, R., Zhou, B., Gulcehre, C., & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023. [27]Levy, O., & Goldberg, Y. (2014). Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Vol. 2, pp. 302-308). [28]Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. Retrieved from https://arxiv.org/abs/1301.3781. [29]Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463). New York: ACM press. [30]Salton, G., & McGill, M. J. (1986). Introduction to modern information retrieval. [31]Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391-407. [32]Hofmann, T. (1999, July). Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence (pp. 289-296). Morgan Kaufmann Publishers Inc. [33]Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022. [34]Sandhya, N., Lalitha, Y. S., Sowmya, V., Anuradha, K., & Govardhan, A. (2011). Analysis of stemming algorithm for text clustering. International Journal of Computer Science Issues (IJCSI), 8(5), 352. [35]Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. [36]Fukunaga, K., & Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on information theory, 21(1), 32-40. [37]Swartout, W., & Tate, A. (1999). Ontologies. IEEE Intelligent Systems and Their Applications, 14(1), 18-19. [38]Staab, S., & Studer, R. (Eds.). (2010). Handbook on ontologies. Springer Science & Business Media. [39]Arthur, D., & Vassilvitskii, S. (2007, January). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (pp. 1027-1035). Society for Industrial and Applied Mathematics. [40]Fukunaga, K., & Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on information theory, 21(1), 32-40. [41]Trappey, C. V., & Trappey, A. J. (2015). Collective intelligence applied to legal e-discovery: A ten-year case study of Australia franchise and trademark litigation. Advanced Engineering Informatics, 29(4), 787-798. [42]Kennett, W. (1999). The International and Comparative Law Quarterly, Vol. 48, No. 4, pp. 966-969. [43]Dobie, A. M. (1914). Venue in the United States District Court. Virginia Law Review, 1-17. [44]Fukunaga, K. & Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32–40. [45]Roitblat, H. L., Kershaw, A., & Oot, P. (2010). Document categorization in legal electronic discovery: computer classification vs. manual review. Journal of the American Society for Information Science and Technology, 61(1), 70-80. [46]Cheng, P., Wang, S., Ma, J., Sun, J., & Xiong, H. (2017, April). Learning to recommend accurate and diverse items. In Proceedings of the 26th International Conference on World Wide Web (pp. 183-192). International World Wide Web Conferences Steering Committee. [47]Formula One Licensing v Purple Interactive, No. C 00–2222 MMC. (2001) [48]EU Intellectual Property Office (EUIPO) (2019). Retrieved from https://ec.europa.eu/growth/content/trends-trade-counterfeit-and-pirated-goods-updated-picture_en [49]林哲丞 (2019) 具深度學習能力之多元商標相似性混淆判定方法與系統 (指導教授:張瑞芬) 碩士論文,國立清華大學,工業工程與工程管理學系。 [50]林凱文 (2018), 分析商標侵權與判例推薦系統 (指導教授:張瑞芬),碩士論文,國立清華大學,工業工程與工程管理學系。
|