# Itemization will be interrupted when I insert an equation [duplicate]

When I insert an equation in a item, there will be no indent for the next items just like this.

There are some head file included.

\documentclass[conference]{IEEEtran}
\IEEEoverridecommandlockouts
\usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
\usepackage{graphicx}
\usepackage{textcomp}

\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 of Algorithm
\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\begin{document}

\begin{itemize}
\item \textbf{Support Vector Machine (SVM).} SVMs are a set of supervised learning methods, and a SVM constructs a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which used for classification, regression and outliers detection. Intuitively, a good separation is achieved by the hyper
\begin{align} \text{minimize }& \frac{1}{n}\sum\limits_{i=1}^{n}{{{\zeta }_{i}}+}\lambda {{\left\| \mathbf{w} \right\|}^{2}} \\ \text{subject to }& {{y}_{i}}(\mathbf{w}\cdot {{\mathbf{x}}_{i}}-b)\ge 1- {{\zeta }_{i}} \\ & {{\zeta }_{i}}\ge 0,i=1,...,n \\ \end{align}
where $\mathbf{x}_i$ is a $p$-dimensional real vector; $y_i$ is either
-1 or 1, denoting the different classes respectively, and $n$ is the
length of a training dataset. $\mathbf{w}$ is the normal vector to th
hyperplane. For each $i\in\{1,...,n\}$, a variable $\zeta_{i}=\text{max} (0,1-y_i(\mathbf{w}\cdot {{\mathbf{x}}_{i}}-b))$ is introduced and it is the
smallest nonnegative number satisfying {{y}_{i}}(\mathbf{w}\cdot
{{\mathbf{x}}_{i}}-b)\ge 1-{{\zeta }_{i}}. Moreover, $\lambda$ is a
sufficiently small value yields the hard-margin classifier for linearly
classifiable input data. This is called the primal problem [].

\item \textbf{Nearest Neighbors.} The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these.
\end{document}

• Your MWE is incomplete. Could you please complete it? – user121799 Apr 2 '18 at 3:06
• Thanks, but it throws errors. Does the code compile without errors on your machine? – user121799 Apr 2 '18 at 3:22
• Sorry about that, it was totally my fault. And the problem is solved now, thank you very much! – LC Dai Apr 2 '18 at 3:38

Here is your fixed code. There were so many errors and problems that I don't even know where to start. It is probably better if you pick up an introductory guide on LaTeX before proceeding to write.

\documentclass[conference]{IEEEtran}
\IEEEoverridecommandlockouts
\usepackage{amsmath}
\begin{document}

\begin{itemize}
\item \textbf{Support Vector Machine (SVM).}  SVMs are a set of
supervised learning methods, and a SVM constructs a hyper-plane or
set of hyper-planes in a high or infinite dimensional space, which
used for classification, regression and outliers
detection. Intuitively, a good separation is achieved by the hyper
\begin{aligned} \text{minimize}\quad & \frac{1}{n} \sum\limits_{i=1}^n \zeta_i + \lambda \left\| \mathbf{w} \right\|^2 \\ \text{subject to}\quad & y_i (\mathbf{w} \cdot \mathbf{x}_i - b) \ge 1 - \zeta_i \\ & \zeta_i \ge 0,i=1,\dotsc,n \end{aligned}
where $\mathbf{x}_i$ is a $p$-dimensional real vector; $y_i$ is
either $-1$ or $1$, denoting the different classes respectively, and
$n$ is the length of a training dataset.  $\mathbf{w}$ is the normal
vector to th hyperplane. For each $i\in\{1,\dotsc,n\}$, a variable
$\zeta_i=\max[0,1-y_i(\mathbf{w}\cdot \mathbf{x}_i - b)]$ is
introduced and it is the smallest nonnegative number satisfying
$y_i(\mathbf{w} \cdot \mathbf{x}_i - b) \ge 1 - \zeta_i$. Moreover,
$\lambda$ is a sufficiently small value yields the hard-margin
classifier for linearly classifiable input data. This is called the
primal problem [].

\item \textbf{Nearest Neighbors.} The principle behind nearest
neighbor methods is to find a predefined number of training samples
closest in distance to the new point, and predict the label from
these.
\end{itemize}

\end{document}


• Wow, thank you so much. It's just the equation problem. Actually I have deleted most of the codes and did not check it out and compile it. I will be more careful next time. – LC Dai Apr 2 '18 at 3:36