\documentclass{article}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{amsmath}
\renewcommand{\algorithmicrequire}{\textbf{Input:}} % Use Input in the format
%of Algorithm
\renewcommand{\algorithmicensure}{\textbf{Output:}} % Use Output in the format
\usepackage[section]{placeins}
\begin{document}
\section{Introduction}
\section{Preliminaries}
\section{Models}
\subsection{Notations}
\begin{algorithm}[htb] \label{myAlg}
\caption{ Framework of ensemble learning for our system.}
\label{alg:Framwork}
\begin{algorithmic}[2]
\Require
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
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}
\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 $E_n$;
\end{algorithmic}
\end{algorithm}
\section{conclusion}
In this paper, we designed an Algorithm \ref{myAlg}.
\end{document}
一周热门 更多>