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个人信息：Personal Information
教授 博士生导师 研究生导师
主要任职：数学与统计学院教授委员会主任
其他任职：中国数学会第十二届理事，陕西省数学会常务理事，陕西省运筹学学会常务理事，
性别：男
毕业院校：西北工业大学
学历：博士研究生毕业
学位：博士学位
在职信息：在岗
所在单位：数学与统计学院
入职时间：2004-09-30
学科：应用数学 概率论与数理统计
办公地点：beoplay体育提现南校区行政辅楼212室
联系方式：西电工作邮箱：ylyang@mail.xidian.edu.cn
电子邮箱：
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论文成果
当前位置: 中文主页 >> bepaly手机下载 >> 论文成果- [1]Youlong Yang, Mengxiao Ding.Decision function with probability feature weighting based on Bayesian network for multi-label classification.Neural Computing & Applications.2018
- [2]Complexity of concept classes induced by discrete Markov networks and Bayesian networks.Pattern Recognition.2018,82 (1):31-37
- [3]Fuzzy rule-based oversampling technique for imbalanced and incomplete data learning.Knowledge-Based Systems.2018,58 (８):154–174
- [4]Maximum relevance minimum common redundancy feature selection for nonlinear data.Information Sciences.2017,409 (10):68-86
- [5]Stochastic correlation coefficient ensembles for variable selection.Journal of Applied Statistics.2017,44 (10):1721-1742
- [6]Structure-Learning of Causal Bayesian Networks Based on Adjacent Nodes.International Journal on Artificial Intelligence Tools.2013,22 (2):100-104
- [7]Youlong Yang , Dandan Yan, Junhang Zhao.Optimal path selection approach for fuzzy reliable shortest path problem.Journal of Intelligent & Fuzzy Systems.2017,32 (1):197-205
- [8]Youlong Yang and Yan Wu.VE Dimension Induced by Bayesian Networks over the Boolean Domain.Pattern Analysis and Applications.2014,17 (4):799-807
- [9]Youlong Yang and Yan Wu.On the Properties of Concept Classes Induced by some Multiple-valued Bayesian Networks.Information Sciences.2012,184 (２):155-165
- [10]Youlong Yang,Yan Wu.VC Dimension and Inner Product Space Induced by Bayesian networks.International Journal of Approximate Reasoning.2009,50 (7):1036-1045
- [11]Youlong Yang,Yan Wu.Inner Product Space and Concept Classes Induced by Bayesian Networks.Acta Applicandae Mathematicae.2009,106 (3):337–348
- [12]Yang, YouLong，Che, JinXing，Li, YanYing，Zhao, YanJun，Zhu, SuLing.An incremental electric load forecasting model based on support vector regression.Energy.2016,113 :796-808
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