面向大规模数据属性效应控制的核心向量回归机
发布时间：2018-12-11 17:36
【摘要】：属性效应在现实生活中广泛存在,如果不加以控制,将会严重影响回归学习的性能.针对大规模数据属性效应控制的非线性回归学习问题,提出了快速等均值核心向量回归机(fast equal mean-core vector regression,FEM-CVR).首先基于间隔最大化目标学习准则,通过施加等均值约束条件,提出了等均值支持向量回归机(equal mean-support vector regression,EM-SVR).在此基础上,证明了EMSVR等价于一个中心约束最小包含球(center constrained-minimum enclosing ball,CC-MEB)问题,然后通过引入近似最小包含球理论,得到原始输入数据集的压缩集即核心集(core set),进一步提出了针对大规模数据属性效应控制的最小包含球快速非线性回归学习方法 FEM-CVR,并从理论上对相关性质进行了深入分析.实验表明:该方法能够快速处理针对大规模数据属性效应控制的非线性回归学习问题,具有较好的泛化能力,并且其时间复杂度上限与数据集大小无关,仅与最小包含球近似参数ε-有关.
[Abstract]:Attribute effect exists widely in real life, if it is not controlled, it will seriously affect the performance of regression learning. A fast equal-mean kernel vector regression machine (fast equal mean-core vector regression,FEM-CVR) is proposed for nonlinear regression learning of attribute effect control in large-scale data. Based on the goal learning criterion of maximizing the interval, the equal-mean support vector regression machine (equal mean-support vector regression,EM-SVR) is proposed by applying the equal mean constraint condition. On this basis, it is proved that EMSVR is equivalent to a central constrained minimum inclusion sphere (center constrained-minimum enclosing ball,CC-MEB) problem. Then, by introducing the theory of approximate minimum inclusion sphere, the compressed set of the original input data set, that is, the core set (core set), is obtained. Furthermore, a fast nonlinear regression learning method, FEM-CVR, is proposed for attribute effect control of large scale data, and the related properties are analyzed theoretically. Experiments show that this method can deal with the nonlinear regression learning problem for large-scale data attribute effect control quickly, and has good generalization ability, and the upper limit of time complexity is independent of the size of data set. It is only related to the minimal inclusion sphere approximation parameter 蔚 -.
【作者单位】： 江南大学数字媒体学院;湖北交通职业技术学院交通信息学院;
【基金】：国家自然科学基金项目(61300151,61572236) 江苏省杰出青年基金项目(BK20140001) 江苏省自然科学基金项目(BK20130155,BK20151299)~~
【分类号】：TP181
本文编号：2372950
[Abstract]:Attribute effect exists widely in real life, if it is not controlled, it will seriously affect the performance of regression learning. A fast equal-mean kernel vector regression machine (fast equal mean-core vector regression,FEM-CVR) is proposed for nonlinear regression learning of attribute effect control in large-scale data. Based on the goal learning criterion of maximizing the interval, the equal-mean support vector regression machine (equal mean-support vector regression,EM-SVR) is proposed by applying the equal mean constraint condition. On this basis, it is proved that EMSVR is equivalent to a central constrained minimum inclusion sphere (center constrained-minimum enclosing ball,CC-MEB) problem. Then, by introducing the theory of approximate minimum inclusion sphere, the compressed set of the original input data set, that is, the core set (core set), is obtained. Furthermore, a fast nonlinear regression learning method, FEM-CVR, is proposed for attribute effect control of large scale data, and the related properties are analyzed theoretically. Experiments show that this method can deal with the nonlinear regression learning problem for large-scale data attribute effect control quickly, and has good generalization ability, and the upper limit of time complexity is independent of the size of data set. It is only related to the minimal inclusion sphere approximation parameter 蔚 -.
【作者单位】： 江南大学数字媒体学院;湖北交通职业技术学院交通信息学院;
【基金】：国家自然科学基金项目(61300151,61572236) 江苏省杰出青年基金项目(BK20140001) 江苏省自然科学基金项目(BK20130155,BK20151299)~~
【分类号】：TP181
【相似文献】
相关期刊论文 前2条
1 罗佳;薛青;唐志武;;作战仿真数据属性离散化研究[J];计算机仿真;2013年09期
2 胡峰;张杰;刘静;肖大伟;;一种基于Rough集的海量数据属性约简方法[J];重庆邮电大学学报(自然科学版);2009年04期
，本文编号：2372950
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2372950.html
