Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且迅速
计算方法。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且迅速
计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表,
样方法可以有效地减少局部
值点,提高归一化互信息测度
性能。
声:以上例句、词性分类均由互联网资源自动生成,部分未经过人工审核,其表达内容亦不代表本软件
观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且迅速
计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表,
样方法可以有效地减少局部
值点,提高归一化互信息测度
性能。
声:以上例句、词性分类均由互联网资源自动生成,部分未经过人工审核,其表达内容亦不代表本软件
观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且收敛迅速的计算法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样法
以有效地减少局部
值点,提高归一化互
度的收敛性能。
声明:以上例句、词性分类均由互联网资源自动生成,部分未经过人工审核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
条件共轭梯度法是一种精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法可以有效地减少局部值点,提高归一化互信息测度的收敛性能。
声明:以上例、词性分类均由互联网资源自动生成,部分未经过
核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯法
一种精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法可以有效地减少局部值点,提高归一化互信息测
的收敛性能。
声明:以上例句、词性分类均由互联网资源自,部分未经过人工审核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法有效地减少局部
值点,提高归一
息测度的收敛性能。
声明:上例句、词性分类均由
联网资源自动生成,部分未经过人工审核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共法是一种精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法可以有效地减少局值点,提高归一化互信息测
的收敛性能。
声明:以上例句、词性类均由互联网资源自动生
,
未经过人工审核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法是一种精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法可以有效地减少局部值点,提高归一化互信息测度的收敛性能。
声明:以上例句、词性分类均由互联网资源自动生成,部分未经过人工审核,其表达不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯度法精确并且收敛迅速的计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,新抽样方法可以有效地减少局部值点,提高归
化互信息测度的收敛性能。
声明:以上例句、词性分类均由互联网资生成,部分未经过人工审核,其表达内容亦不代表本软件的观点;若发现问题,欢迎向我们指正。
Preconditioned con- jugate gradient algorithm is a fast and accurate computational algorithm.
预条件共轭梯法是一种精确并且
敛迅速
计算方法。
The results of tests show that the new subsampling method outperforms other subsampling methods in less extrema and convergence performance of the normalized mutual information.
实验结果表明,方法可以有效地减少局部
值点,提高归一化互信息测
敛性能。
声明:以上例句、词性分类均由互联网资源自动生成,部分未经过人工审核,其表达内容亦不代表本软件观点;若发现问题,欢迎向我们指正。