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隐私团校准的模糊MEB学习 被引量：1

Privacy cloud calibration fuzzy learning for MEB
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摘要 在一定条件下,基于最小累积平方误差(ISE)准则的高斯核密度估计与最小包含球(MEB)等价.在此基础上提出了一种含团状隐私数据保护的MEB学习方法,称为隐私团校准的MEB(PCC-MEB)方法;同时,通过引入模糊隶属度函数将PCC-MEB拓展为模糊的PCC-MEB(FPCC-MEB),从而解决二类及多类问题中区域不可分问题.人造和真实数据集上的实验结果表明,所提出方法具有较好的性能. Under given conditions,Gaussian kernel density estimate with minimum integrated square error（ISE） criterion can be equivalent to the minimum enclosing ball（MEB）.Based on this conclusion,a learning method of MEB with privacy cloud data is proposed,called privacy cloud calibration MEB（PCC-MEB）.Meanwhile,PCC-MEB is extended to fuzzy privacy cloud calibration MEB（FPCC-MEB） by introducing a fuzzy membership function,which can resolve unclassifiable zones among classes.Experimental results on the artificial and real-word data sets show the effectiveness of presented method.
作者 胡文军 王士同
机构地区 江南大学信息工程学院 湖州师范学院信息与工程学院
出处 《控制与决策》 EI CSCD 北大核心 2012年第2期221-226,共6页 Control and Decision
基金 国家自然科学基金项目(60903100 60975027) 江苏省普通高校研究生科研创新计划项目(CXZZ11 0483)
关键词 最小包含球 核密度估计 隐私数据团 核方法 模糊 minimum enclosed ball（MEB） kernel density estimator privacy data cloud kernel method fuzzy
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献17

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共引文献1

同被引文献14

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控制与决策
2012年 第2期

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