副教授
招生学科专业:
计算机科学与技术 -- 【招收硕士研究生】 -- 计算机科学与技术学院
软件工程 -- 【招收硕士研究生】 -- 计算机科学与技术学院
电子信息 -- 【招收硕士研究生】 -- 计算机科学与技术学院
学历:南京航空航天大学
学位:工学博士学位
所在单位:计算机科学与技术学院/人工智能学院/软件学院
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所属单位:计算机科学与技术学院/人工智能学院/软件学院
发表刊物:Lect. Notes Comput. Sci.
摘要:The influencing factors of the ice hockey match result are complex, and there is a nonlinear relationship with the relevant predictive indicators. The input characteristics and parameter selection of the model have important influence on the prediction performance. Based on this, this paper proposes a support vector machine ice hockey situation prediction model based on principal component analysis, hybrid genetic algorithm and particle swarm optimization. This model uses principal component analysis to perform principal component analysis on the original features, this can reduce the dimensions of the original features effectively. Using hybrid genetic algorithm and particle swarm algorithm to optimize the parameters of support vector machine to establish a predictive model. The simulation results show that when principal component analysis is used to reduce the input features, the running time of the SVM prediction model based on principal component analysis, hybrid genetic algorithm and particle swarm optimization is reduced. Compared to a single genetic algorithm optimization parameter or a particle swarm optimization parameter support vector machine prediction model, the prediction accuracy and stability are significantly improved. © 2018, Springer Nature Switzerland AG.
ISSN号:0302-9743
是否译文:否
发表时间:2018-01-01
合写作者:Li, Mengying,Cheng, Sijia
通讯作者:薛善良