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/usepackage{algorithm}               //format of the algorithm
/usepackage{algorithmic}             //format of the algorithm
/usepackage{multirow}                //multirow for format of table
/usepackage{amsmath}
/usepackage{xcolor}
/DeclareMathOperator*{/argmin}{argmin}         //argmin»òargmax¹«Ê½µÄÅŰæ
/renewcommand{/algorithmicrequire}{/textbf{Input:}}   //Use Input in the format of Algorithm
/renewcommand{/algorithmicensure}{/textbf{Output:}}  //UseOutput in the format of Algorithm

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/usepackage{graphics}
/usepackage{graphicx}
/usepackage{epsfig}

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/begin{algorithm}[htb]         %Ëã·¨µÄ¿ªÊ¼
/caption{ Framework of ensemble learning for our system.}             %Ëã·¨µÄ±êÌâ
/label{alg:Framwork}                  %¸øËã·¨Ò»¸ö±êÇ©£¬ÕâÑù·½±ãÔÚÎÄÖжÔËã·¨µÄÒýÓÃ
/begin{algorithmic}[1]                %Õâ¸ö1 ±íʾÿһÐж¼ÏÔʾÊý×Ö
/REQUIRE ~~//                          %Ëã·¨µÄÊäÈë²ÎÊý£ºInput
    The set of positive samples for current batch, $P_n$;//
    The set of unlabelled samples for current batch, $U_n$;//
    Ensemble of classifiers on former batches, $E_{n-1}$;
/ENSURE ~~//                           %Ëã·¨µÄÊä³ö£ºOutput
    Ensemble of classifiers on the current batch,  $E_n$;
/STATE Extracting the set of reliable negative and/or positive samples $T_n$ from $U_n$  with help of $P_n$; /label{code:fram:extract}      %Ëã·¨µÄÒ»¸ö³ÂÊö£¬¶ÔÓ¦Ëã·¨µÄÒ»¸ö²½Öè»ò¹«Ê½Ö®ÀàµÄ£» /label{ code:fram:extract }¶Ô´ËÐеıê¼Ç£¬·½±ãÔÚÎÄÖÐÒýÓÃËã·¨µÄij¸ö²½Öè
/STATE Training ensemble of classifiers $E$ on $T_n /cup P_n$, with help of data in former batches; /label{code:fram:trainbase}
/STATE $E_n=E_{n-1}/cup E$; /label{code:fram:add}
/STATE Classifying samples in $U_n-T_n$ by $E_n$; /label{code:fram:classify}
/STATE Deleting some weak classifiers in $E_n$ so as to keep the capacity of $E_n$; /label{code:fram:select}
/RETURN $E_n$;                %Ëã·¨µÄ·µ»ØÖµ
/end{algorithmic}
/end{algorithm}

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/begin{algorithm}[h]
    /caption{An example for format For /& While Loop in Algorithm}
    /begin{algorithmic}[1]
        /FOR{each $i/in [1,9]$}
            /STATE initialize a tree $T_{i}$ with only a leaf (the root);//
            /STATE $T=T/bigcup T_{i};$//
        /ENDFOR
      
        /FORALL {$c$ such that $c/in RecentMBatch(E_{n-1})$} /label{code:TrainBase:getc}
            /STATE  $T=T /cup PosSample(c)$; /label{code:TrainBase:pos}
        /ENDFOR;
      
        /FOR{$i=1$; $i<n$; $i++$ }
            /STATE $//$ Your source here;
        /ENDFOR
      
        /FOR{$i=1$ to $n$}
            /STATE $//$ Your source here;
        /ENDFOR
      
        /STATE  $//$ Reusing recent base classifiers. /label{code:recentStart}
        /WHILE {$(|E_n| /leq L_1 )and( D /neq /phi)$}
            /STATE  Selecting the most recent classifier $c_i$ from $D$;
            /STATE  $D=D-c_i$;
            /STATE  $E_n=E_n+c_i$;
        /ENDWHILE /label{code:recentEnd}
      
    /end{algorithmic}
/end{algorithm}
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david012

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6Â¥2017-08-01 17:27:16
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