冰河的博客

排版可能需要的包：

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或argmax公式的排版

enewcommand{algorithmicrequire}{ extbf{Input:}} //Use Input in the format of Algorithm

enewcommand{algorithmicensure}{ extbf{Output:}} //UseOutput in the format of Algorithm

排版图片可能需要的包：

usepackage{graphics}

usepackage{graphicx}

usepackage{epsfig}

算法的排版举例：

\begin{algorithm}[htb] %算法的开始

caption{ Framework of ensemble learning for our system.} %算法的标题

label{alg:Framwork} %给算法一个标签，这样方便在文中对算法的引用

\begin{algorithmic}[1] %这个1 表示每一行都显示数字

REQUIRE ~~\ %算法的输入参数：Input

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 ~~\ %算法的输出：Output

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} %算法的一个陈述，对应算法的一个步骤或公式之类的； label{ code:fram:extract }对此行的标记，方便在文中引用算法的某个步骤

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 $E_n$; %算法的返回值

end{algorithmic}

end{algorithm}

排版效果图：

在文中对算法和算法的某个步骤的引用：Therefore, in step
ef{code:fram:extract} of algorithm
ef{alg:Framwork}, we extract $T_n$, a set of reliable negative samples

1、 For和While循环语句的排版举例

（1） 排版效果图

（2）排版代码

\begin{algorithm}[h]

caption{An example for format For & While Loop in Algorithm}

\begin{algorithmic}[1]

FOR{each $iin [1,9]$}

STATE initialize a tree $T_{i}$ with only a leaf (the root);\

STATE $T=Tigcup T_{i};$\

ENDFOR

FORALL {$c$ such that $cin RecentMBatch(E_{n-1})$} 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 {$(|E_n| leq L_1 )and( D
eq phi)$}

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}