期刊文献+

融合通信不良信息个性化管控方法研究

Research on Personalized Control Method of RCS Adverse Information
下载PDF
导出
摘要 从实现融合通信不良信息个性化管控的需求出发,研究了基于个性化需求的治理体系组网,设计了云端联动治理体系架构和体系结构,分析了网络侧与终端侧联动管控不良信息的方法。研究并开发实现了云端系统和终端系统不良信息个性化管控初步功能,举例说明了个性化管控功能的消息流程。 Starting from the realization of personalized demand of RCS adverse information control, this paper studied the system networking based on the individual needs of the management of adverse information, designed the cloud side and the terminal side linkage governance architecture and functional structure, analyzed the network side and the terminal side linkage control method of adverse information, and developed initial control function of adverse information in the cloud system and the terminal system, illustrated message flow of personalized control function of adverse information by given example.
作者 徐云恒
出处 《移动通信》 2016年第12期33-38,共6页 Mobile Communications
关键词 融合通信 云端系统 终端系统 rich communication suite system in cloud terminal system
  • 相关文献

参考文献10

  • 1工业和信息化部.通信短信息服务管理规定[s].2015.
  • 2中国移动通信集团.融合通信安全总体技术要求[z].2014.
  • 3中国移动通信集团.融合通信不良信息安全管控平台大区监控子系统设备规范分册[Z].2015.
  • 4中国移动通信集团.融合通信设备规范--新消息平台分册V1.0.o[z].2015.
  • 5中国移动通信集团.融合通信接VI规范--终端.平台接口分册[z].2014.
  • 6工业和信息化部电信研究院.YD/T2254-2011基于用户设置规则的短消息过滤系统测试方法[s].2011.
  • 7朱军,胡文波.贝叶斯机器学习前沿进展综述[J].计算机研究与发展,2015,52(1):16-26. 被引量:71
  • 8刘露,彭涛,左万利,戴耀康.一种基于聚类的PU主动文本分类方法[J].软件学报,2013,24(11):2571-2583. 被引量:24
  • 9工业和信息化部电信研究院.YD/T2035.2009移动终端垃圾短消息过滤技术要求[S].2009.
  • 10马金鑫,袁丁.基于NDIS内核过滤技术的截获网络包技术研究[J].通信技术,2010,43(2):137-140. 被引量:2

二级参考文献44

  • 1高泽胜,陶宏才.基于NDIS-HOOK与SPI的个人防火墙研究与设计[J].计算机应用研究,2004,21(11):279-281. 被引量:9
  • 2侯功华,赵远东.基于NDIS中间层的包过滤的研究与设计[J].微计算机信息,2006(12X):141-143. 被引量:15
  • 3Liu W, Wang T. Online active multi-field learning for efficient email spam filtering. Knowledge and Information Systems, 2012, 33(1):117-136. [doi: 10.1007/s 10115-011-0461-x].
  • 4Fumera G, Pillai I, Roli F. Spam filtering based on the analysis of text information embedded into images. Journal of Machine Learning Research, 2006,7:2699-2720.
  • 5Qi XG, Davison BD. Web page classification: Feature and algorithms. ACM Computing Surveys, 2009,41(2):Article 12. [doi: 10. 1145/1459352.1459357].
  • 6Anotonellis I, Bouras C, Poulopoulos V. Personalized news categorization through scalable text classification. Frontiers of WWW Research and Development-APWEB, Lecture Notes in Computer Science, 2006,3841:391-401. [doi: 10.1007/11610113 35].
  • 7Hu M, Liu B. Mining and summarizing customer review. In: Proc. of the ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM, 2004. 168-177. [doi: 10.1145/1014052.1014073].
  • 8Kim S, Hovy E. Determining the sentiment of opinions. In: Proc. of the Int’l Conf. on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2004. [doi: 10. 3115/1220355.1220555].
  • 9Schohn G, Cohn D. Less is more: Active learning with support vector machines. In: Proc. of the 17th Int’l Conf. on Machine Learning. San Francisco: Morgan Kaufmann Publishers, Inc., 2000. 839-846.
  • 10Liu B, Lee WS, Yu PS, Li XL. Partially supervised classification of text documents. In: Sammut C, Hoffmann AG, eds. Proc. of the 19th Int’l Conf. on Machine Learning. San Francisco: Morgan Kaufmann Publishers, Inc., 2002. 387-394.

共引文献94

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部