吴鲁辉,李凡长,张莉.多李群覆盖学习优化算法[J].计算机科学,2018,45(1):108-112
多李群覆盖学习优化算法
Optimization Algorithm of Multiply Lie Group Covering Learning Algorithm
投稿时间:2017-03-03  修订日期:2017-05-23
DOI:10.11896/j.issn.1002-137X.2018.01.017
中文关键词:  李群,覆盖学习,道路交叉,核学习算法
英文关键词:Lie group,Covering learning algorithm,Road cross,Kernel learning algorithm
基金项目:
作者单位E-mail
吴鲁辉 苏州大学计算机科学与技术学院 江苏 苏州215000 wuluhuiedy@qq.com 
李凡长 苏州大学计算机科学与技术学院 江苏 苏州215000  
张莉 苏州大学计算机科学与技术学院 江苏 苏州215000  
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中文摘要:
      目前,已针对李群多连通空间上的道路交叉问题提出了多李群核覆盖学习算法,降低了道路交叉情况,使得分类正确率有了显著提高。但是,核学习算法的性能依赖于核函数的选择。考虑利用李群同态映射将原始李群样本映射到目标李群空间中,使在目标李群空间中不同单连通空间上的道路的关联度最小化,同一单连通空间上的道路的关联度最大化,从而减少道路交叉问题。
英文摘要:
      In the previous study,a multiply Lie group kernel covering learning algorithm was proposed to reduce the intersection of roads and improve the correctness of classification for multi-connected spaces.However,the performance of the kernel learning algorithm depends on the choice of kernel function.In this paper,it is considered that the original Lie group samples are mapped to the target Lie group space by the Lie group homomorphic mapping,the degree of the road association is minimized in different single connected spaces in the target Lie group space,and the correlation degree of the road in the same single connected space is maximized,in order to reduce road cross problems.
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