思茅师专学报字魏玲

第一作者[1] 魏玲, 师义民. 巴斯卡分布参数的Bayes估计. 纯粹数学与应用数学, 1999, 15(2): 13-16.[2] 魏玲, 师义民. 对数正态分布无失效数据的可靠性分析. 西北大学学报(自然科学版), 2000, 30(1): 1-3.[3] Wei Ling, Qi Jian-jun, Shi Yi-min. The EB Estimation of Scale-Parameter for the Two-Parameter Exponential Distribution Under the Type-Ⅱ Censoring Life Test. MATHEMATICA APPLICATA, 2001, 14(4): 66-70.[4] LING WEI, JIAN-JUN QI, WEN-XIU ZHANG. Knowledge Discovery of Decision Table Based on Support Vector Machine. In: Proceedings of 2003 International Conference on Machine Learning and Cybernetics. IEEE, 2003, 1195-1200.[5] 魏玲, 祁建军, 张文修. 基于支持向量机的决策系统知识发现. 西安交通大学学报, 2003, 37(10): 995-998.[6] 魏玲, 张文修. 基于支持向量机集成的分类. 计算机工程, 2004, 30(13): 1-2,17.[7] 魏玲, 师义民. LINEX损失下一类分布族参数渐近最优的EB检验. 西北大学学报(自然科学版), 2004, 34(5): 517-521.[8] Ling Wei, Wen-xiu Zhang. Attribute Reduction Based on Equivalence Relation Defined on Attribute Set and its Power Set. In: B. Dunin-Keplicz, A. Jankowski, A. Skowron, and M. Szczuka (eds.). International Workshop on Monitoring, Security and Rescue Techniques in Multiagent Systems MSRAS 2004, Advances in Soft Computing. Berlin Heidelberg: Springer-Verlag, 2005, 317-326.[9] Ling Wei, Wen-xiu Zhang. A Novel Statistical Method on Decision Table Analysis. Lecture Notes in Computer Science (AI 2004), 2004, 3339: 973-978.[10] 魏玲, 张文修. 决策表分析的统计依据. 计算机科学, 2005, 32(4): 19-21.[11] 魏玲, 祁建军, 张文修. 概念格与粗糙集的关系研究. 计算机科学, 2006, 33(3): 18-21.[12] 魏玲. 概念格属性约简理论与方法.// 张文修, 姚一豫, 梁怡. 粗糙集与概念格. 西安: 西安交通大学出版社, 2006: 357-380.[13] 魏玲, 张文修. 粗糙集约简的闭算子方法. 计算机科学, 2007, 34(1): 159-162,182.[14] Ling Wei, Hong-Ru Li, Wen-Xiu Zhang. Knowledge Reduction Based on the Equivalence Relations Defined on Attribute Set and Its Power Set. Information Sciences, 2007, 177(15): 3178-3185.[15] 魏玲, 祁建军, 张文修. 决策形式背景的概念格属性约简. 中国科学E辑: 信息科学, 2008, 38(2): 195-208. WEI Ling, QI Jianjun, ZHANG Wenxiu. Attribute reduction theory of concept lattice based on decision formal contexts. Science in China Series F: Information Science, 2008, 51(7): 910-923.[16] Ling Wei, Jian-Jun Qi. Combination and Decomposition Theories of Formal Contexts Based on Same Attribute Set. Lecture Notes in Computer Science (RSKT 2008), 2008, 5009: 452-459.[17] Ling Wei, Guang Yao, Wei Zhao. Object oriented concept lattice construction through the combination of formal contexts. In: Proceedings of 2009 International Conference on Machine Learning and Cybernetics. IEEE, 2009, 2127-2131.[18] Ling Wei, Xiao-Hua Zhang, Jian-Jun Qi. Granular Reduction of Property-Oriented Concept Lattices. Lecture Notes in Computer Science (ICCS 2010), 2010, 6208: 154-164.[19] Ling Wei, Sheng-Nan Wang, Wei Zhao. Methods to construct several kinds of concept lattices. In: Proceedings of 2010 International Conference on Machine Learning and Cybernetics. IEEE, 2010, 91-96.[20] Ling Wei, Hui-Yan Hong, Wei Zhao. The relationship between property-oriented concept lattice and partition. In: Proceedings of 2010 International Conference on Machine Learning and Cybernetics. IEEE, 2010, 122-127.[21] Ling Wei, Jian-Jun Qi. Relation between concept lattice reduction and rough set reduction. Knowledge-Based Systems, 2010, 23(8): 934-938.[22] Ling Wei, Jing Zhang, Hong-Ying Zhang. Comparison of concept lattice reduction based on discernbility matrixes. In: Proceedings of 2011 International Conference on Machine Learning and Cybernetics. IEEE, 2011, 1118-1123.[23] Ling Wei, Qiang Li. Covering-Based Reduction of Object-Oriented Concept Lattices. Lecture Notes in Computer Science (RSKT 2011), 2011, 6954: 728-733.通讯作者[1] Min-Qian Liu, Ling Wei, Wei Zhao. The Reduction Theory of Object Oriented Concept Lattices and Property Oriented Concept Lattices. Lecture Notes in Computer Science (RSKT 2009), 2009, 5589: 587-593.[2] Shengnan Wang, Ling Wei, Qiang Li. Construct New Lattices Based on Rough Set Theory. In: Proceedings of 2010 2nd International Conference on Information Science and Engineering, IEEE, 2010, 5013-5016.[3] Kesheng Wu, Ling Wei. Attribute Reduction Based on Interval-valued Formal Contexts. In: Proceedings of 2010 2nd International Conference on Information Science and Engineering, IEEE, 2010, 4978-4981.[4] 赵玉锋, 魏玲, 刘敏茜. 多背景横向合并属性特征分析. 西北大学学报(自然科学版), 2009, 39(2): 181-185.[5] 赵玉锋, 魏玲, 王磊. 模糊形式背景横向合并概念的生成. 计算机科学, 2009, 36(8A): 116-120.[6 姚广, 魏玲, 王磊. 合成背景的面向属性概念格生成. 西北大学学报(自然科学版), 2010, 40(1): 1-4.[7] 王磊, 魏玲, 姚广. 横向合成背景的概念生成. 西北大学学报(自然科学版), 2010, 40(2): 195-198.[8] 王彬弟, 魏玲. 基于关联格的概念格约简理论. 山东大学学报(理学版), 2010, 45(9): 20-26.[11] 孙昱薇, 魏玲，仇代远. 基于基概念的面向属性概念格建格方法. 西北大学学报(自然科学版), 2011, 41(1): 11-14,18.第二作者[1] 师义民, 魏玲, 肖华勇. 冷贮备串联系统可靠性指标的估计. 西北工业大学学报, 2001, 19(1): 76-79.[2] 赵炜, 魏玲, 许军, 张毅. 线性加权评价与聚类分析理论及应用. 纯粹数学与应用数学, 2002, 18(2): 121-125.[3] 张文修, 魏玲, 徐萍. 广义信息系统上的Rough集理论. 模糊系统与数学, 2004, 18(9): 29-33.[4] 张文修, 魏玲, 祁建军. 概念格的属性约简理论与方法. 中国科学E辑:信息科学, 2005, 35(6): 628-639. ZHANG Wenxiu, WEI Ling, QI Jianjun. Attribute reduction theory and approach to concept lattice. Science in China Series F-Information Science, 2005, 48(6): 713-726.[5] Jian-Jun Qi, Ling Wei, Zeng-Zhi Li. A Partitional View of Concept Lattice. Lecture Notes in Computer Science (RSFDGrC 2005), 2005, 3641: 74-83.[6] Wen-Xiu Zhang, Ling Wei, Jian-Jun Qi. Attribute Reduction in Concept Lattice Based on Discernibility Matrix. Lecture Notes in Computer Science (RSFDGrC 2005), 2005, 3642: 157-165.[7] Jian-Jun Qi, Ling Wei, Zeng-Zhi Li. A Computational Model of Trust. Computer Science and Telecommunications, 2005, 7(3): 16-20.[8] 张文修, 魏玲, 祁建军, 仇国芳. 概念格约简泛化与概念粒逼近.// 苗夺谦, 王国胤, 刘清, 林早阳, 姚一豫. 粒计算：过去、将来与展望. 北京: 科学出版社, 2007: 243-274.[9] Jian-Jun Qi, Ling Wei. Attribute Reduction in Consistent Decision Formal Context. Information Technology Journal, 2008, 7(1): 170-174.[10] Jian-Jun Qi, Ling Wei, Yun-Bo Bai. Composition Of Concept Lattices. In: Proceedings of 2008 International Conference on Machine Learning and Cybernetics. IEEE, 2008, 2274-2279.[11] Jian-Jun Qi, Ling Wei, Yan-Ping Chen. Correlation Analysis Between Objects and Attributes. Lecture Notes in Computer Science (RSKT 2009), 2009, 5589: 594-600.[12] QI Jian-Jun, WEI Ling, LIU Wei. Concept lattice construction through the composition and decomposition of formal context. In: Proceedings of 2010 International Workshop on ROUGH SETS THEORY, 2010, 488-493.[13] Jian-Jun Qi, Ling Wei, Hui-Yan Hong. Concept lattice construction about many-valued contexts. In: Proceedings of 2011 International Conference on Machine Learning and Cybernetics. IEEE, 2011, 1124-1129.