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liulan2013
гæ (СÓÐÃûÆø)
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3Â¥2015-04-11 00:01:20
zhxh1997
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liulan2013(×Ô˽µÄè1988´ú·¢): ½ð±Ò+3, ¹ÄÀø½»Á÷ 2015-04-12 02:51:21
liulan2013: ½ð±Ò+17, ¡ïÓаïÖú 2015-04-14 10:55:34
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liulan2013(×Ô˽µÄè1988´ú·¢): ½ð±Ò+3, ¹ÄÀø½»Á÷ 2015-04-12 02:51:21
liulan2013: ½ð±Ò+17, ¡ïÓаïÖú 2015-04-14 10:55:34
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Çë²Î¿¼£º \begin{center} {\bf Algorithm 1}: Estimating coefficients of model \begin{tabular}{lp{75mm}}\toprule[1.5pt] \multicolumn{2}{l}{Input: sample data $\{(x_i,f_i)|i=1,2,\cdots,n\}$}\\ \multicolumn{2}{l}{Output: coefficient \(\mbox{\boldmath $a$} \) = $(a_1,a_2,\cdots,a_n)^{\top}$}\\ \midrule 1. & Initialize coefficient parameter {\mbox{\boldmath $a$}}, using the general least-squares regression given as Eq. (\ref{eqn:cost}).\\ 2. & Compute the weight matrix $\bf W$ using the current solution ${\mbox{\boldmath $a$}}_k$ as ${\bf W} \leftarrow diag(w_1,w_2, \cdots, w_n)$, where $w_i$ is defined in Eq. (\ref{eqn:weight}) and $diag()$ forms a $n \times n$ matrix with $w_1,w_2,\cdots,w_n$ as diagonal elements.\\ 3. & Compute the next solution ${\mbox{\boldmath $a$}}_{k+1}$ of weighted minimum least squares.\\ 4. & Repeat steps 2) and 3) until the solution converges or the number of iterations equals a predetermined cutoff.\\ \bottomrule[1.5pt] \end{tabular} \end{center} |

2Â¥2015-04-03 17:18:04













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