论文专著
[1] LiangDong Hu, LiMin Wang. Using consensus bayesian network to model the reactive oxygen spe...[全部]
[1] LiangDong Hu, LiMin Wang. Using consensus bayesian network to model the reactive oxygen species regulatory pathway. PLOS ONE, 2013.(SCI)
[2]LiMin Wang. Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference. Mathematical Problems in Engineering, 2013.(SCI)
[3] LiMin Wang, GuoFeng Yao. Learning NT Bayesian Classifier Based on Canonical Cover Analysis of Relational Database. Information: An International Interdisciplinary Journal, 2012, 15(1), 165-172. (SCI)
[4] LiMin Wang, GuoFeng Yao. Extracting Logical Rules and Attribute Subset from Confidence Domain. Information: An International Interdisciplinary Journal, 2012, 15(1), 173-180. (SCI)
[5] LiMin Wang. Bayesian Network Inference Based on Functional Dependency Mining of Relational Database. Information: An International Interdisciplinary Journal. 2012, 15(6), 24411-2446. (SCI)
[6] LiMin Wang. Implementation of a scalable decision forest model based on information theory. Expert Systems with Applications, 2011, 38(5): 5981-5985. (SCI)
[7] LiMin Wang, XueBai Zang. Semi-Supervised Learning Based on Information Theory and Functional Dependency Rules of Probability. Advanced Science Letters, 2011, 4(2): 463-468. (SCI)
[8]曹春红, 王利民, 赵大哲. 基于离散元胞蚂蚁算法的几何约束求解技术研究. 电子学报, 2011, 39(5): 1127-1131. (EI)
[9] ChunHong Cao, LiMin Wang, WenHui Li. The Geometric Constraint Solving Based on the Quantum Particle Swarm. Lecture Notes in Artificial Intelligence, 2011, 6401: 582-587. (EI)
[10]LiangDong Hu, LiMin Wang, LiYan Dong. Quantitative Combination of Different Bayesian Networks. Procedia Engineering. 2011, 15(12), 3526–3530. (EI)
[11]ChunHong Cao, ChangSheng Zhang, LiMin Wang. An Improved Particle Swarm Optimization Algorithm for Geometric Constraint Solving Problem. In Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, 2010, 1835-1838. (EI)
[12]王利民. 基于半监督学习的启发式值约简. 控制与决策, 2010, 25(10): 1531-1535. (EI)
[13]LiMin Wang. Towards Efficient Dimensionality Reduction for Evolving Bayesian Network Classifier. Advanced Materials Research, 2010, 108-111: 240-243. (EI)
[14]LiMin Wang. An Adaptive Ensemble Approach for Multi-level Semantic Knowledge Representation. Journal of Information & Computational Science, 2010, 7(1): 9-15. (EI)
[15]LiMin Wang. Class Dependent Feature Scaling Method via Restrictive Bayesian Network Classifier Combination. Journal of Computational Information Systems, 2010, 6(1): 33-38. (EI)
[16]王利民, 臧雪柏, 曹春红. 基于广义信息论的决策森林数据挖掘模型. 吉林大学学报(工学版), 2010, 40(1): 155-158. (EI)
[17]王利民. 基于广义信息论的贝叶斯分类器动态建模. 吉林大学学报(工学版), 39(3): 776-780, 2009. (EI)
[18] Wang LiMin, Xu PeiJuan, Li XiongFei. Learning Hybrid Bayesian Network Based on Divide and Conquer Strategy. Journal of Computational Information Systems, 3(2): 583-590, 2007. (EI )
[19]Wang LiMin, Cao ChunHong, Li XiongFei, Li HaiJun. Inference and Learning in Hybrid Probabilistic Network. Frontier of Computer Science in China, 1(4): 429-435, 2007. (EI )
[20]Wang LiMin, Zhang Zhijun, Cao ChunHong, Dong LiYan. Dimensionality reduction for evolving neural network. Journal of Computational Information Systems. 2(3): 1079-1084, 2006. (EI )
[21]Cao ChunHong, Zhang Bin, Wang LiMin, Song JiaLi, Li WenHui. The Geometric Constraint Solving Based on the Immune Genetic Algorithm. Journal of Computational Information Systems, 26: 787-794, 2006. (EI )
[22]Wang LiMin. Learning Bayesian-Neural Network from Mixed-mode Data. In Proceedings of the 13th International Conference on Neural Information Processing, 680-687, 2006. (SCI)
[23]Cao ChunHong, Zhang Bin, Wang LiMin. The Parametric Design Based on Organizational Evolutionary Algorithm. In Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, 940-944, 2006. (SCI)
[24]Wang LiMin, Cao ChunHong, Li HaiJun. Orthogonally Rotational Transformation for Naive Bayes Learning. In Proceedings of the 2005 International Conference on Computational Intelligence and Security, 145-150, 2005. (SCI)
[25]王利民, 苑森淼。 具有抗噪音能力的增量式混合贝叶斯网络. 仪器仪表学报, 26(3): 2216-225, 2005. (EI)
[2]Wang LiMin, Cao ChunHong, Dong LiYan, Li XiaoLin. Generalized Tree Augmented Naive Bayes. Journal of Computational Information Systems, 1(4): 741-747, 2005. (EI)
[27]李海军, 王钲旋, 王利民. 一种基于贝叶斯测度的有监督离散化方法. 仪器仪表学报, 26(8): 786-789, 2005. (EI)
[28]李百策, 苑森淼, 王利民. 贝叶斯网络的简约模式表达. 仪器仪表学报, 26(10): 1070-1073, 2005. (EI)
[29]Wang LiMin, Li XiaoLin, Cao ChunHong, Yuan SenMiao. Combining Decision Tree and Naive Bayes for Classification. Knowledge-Based Systems, 10: 511-515, 2005. (SCI)
[30]Wang LiMin, Yuan SenMiao. Induction of hybrid decision tree based on post discretization strategy. Progress in Natural Science, 16: 541-545, 2004. (SCI)
[31]Wang LiMin, Yuan SenMiao, Li HaiJun, LiLing. Improving the Performance of Naive Bayes：A Hybrid Approach. In Proceedings of the 23th International Conference on Conceptual Modeling, 327-335, 2004. (SCI)
[32]Wang LiMin, Yuan SenMiao, Li HaiJun, LiLing. Boosting Naive Bayes by Active Learning. In Proceedings of the 2004 International Conference on Machine learning and Cybernetics, 1383-1386, 2004. (EI)[收起]