为了降低支持向量机(SVM)算法在高阶多元位置相移键控(M-ary Position Phase Shift Keying,MPPSK)系统的信号检测复杂度,在分析常用SVM多分类算法的基础上,提出了一种新的具有更低复杂度的类二分法SVM。为了进一步提高高阶MPPSK信号检...为了降低支持向量机(SVM)算法在高阶多元位置相移键控(M-ary Position Phase Shift Keying,MPPSK)系统的信号检测复杂度,在分析常用SVM多分类算法的基础上,提出了一种新的具有更低复杂度的类二分法SVM。为了进一步提高高阶MPPSK信号检测性能,提出一种新的SVM特征向量提取方法,调制矩阵法,并将两种方法结合起来,用于高阶MPPSK系统的信号检测。仿真结果表明:类二分法SVM能显著降低多分类SVM的算法复杂度,调制矩阵选取特征向量法能够显著提高高阶MPPSK系统的检测性能,两种方法结合用于高阶MPPSK系统,可以在有效降低复杂度的前提下保证期望的检测性能。展开更多
In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equ...In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process.展开更多
文摘为了降低支持向量机(SVM)算法在高阶多元位置相移键控(M-ary Position Phase Shift Keying,MPPSK)系统的信号检测复杂度,在分析常用SVM多分类算法的基础上,提出了一种新的具有更低复杂度的类二分法SVM。为了进一步提高高阶MPPSK信号检测性能,提出一种新的SVM特征向量提取方法,调制矩阵法,并将两种方法结合起来,用于高阶MPPSK系统的信号检测。仿真结果表明:类二分法SVM能显著降低多分类SVM的算法复杂度,调制矩阵选取特征向量法能够显著提高高阶MPPSK系统的检测性能,两种方法结合用于高阶MPPSK系统,可以在有效降低复杂度的前提下保证期望的检测性能。
基金Supported by the National Natural Science Foundation of China(10901125,11471253)
文摘In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process.
