文章摘要
基于Cholesky分解的LSSVM在线学习算法
An online learning algorithm for LSSVM based on Cholesky factorization
  
DOI:
中文关键词: 最小二乘支持向量机  在线学习  Cholesky分解  滚动时间窗  系统在线辨识
英文关键词: least square support vector machine  online learning  Cholesky factorization  sliding time window  system online identification
基金项目:国家自然科学基金(10971060);湖南省科学技术厅项目(2011FJ6033)
作者单位
蒋星军1,2,周欣然3,唐钊轶2 1.北京工业大学 计算机学院北京 1000222.湖南广播电视大学 计算机系湖南 长沙 410004 3.中南大学 信息科学与工程学院湖南 长沙 410075 
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中文摘要:
      针对最小二乘支持向量机(LSSVM)用于在线建模时存在的计算复杂性问题,提出一种LSSVM在线学习算法.首先引入了基于Cholesky分解求LSSVM的方法,接着根据在线建模期间核函数矩阵的更新特点,将分块矩阵Cholesky分解用于LSSVM的在线求解,使三角因子矩阵在线更新从而得出一种新的LSSVM在线学习算法.该算法能充分利用历史训练结果,减少计算量.仿真实验显示了这种在线学习算法的有效性.
英文摘要:
      Aiming at the computational complexity of least squares support vector machine(LSSVM)’s online modeling, an online learning algorithm for LSSVM was proposed. First, the solution of LSSVM through the Cholesky factorization was introduced, then the Cholesky factorization of partitioned matrix was applied to the online solution of LSSVM according to the updating character of kernel function matrix during online modelling, and triangle factor matrix was renewed online, consequently, a novel online learning algorithm for LSSVM was obtained. The improved learning algorithm can make full use of the historical training results and reduce the computation amount. The numerical simulation results the validity of the online learning algorithm for LSSVM.
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