Itemization will be interrupted when I insert an equation

Question

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{equation}
\[\begin{align}
\text{minimize  }& \frac{1}{n}\sum\limits_{i=1}^{n}{{{\zeta }_{i}}+}\lambda 
{{\left\| \mathbf{w} \right\|}^{2}} \\ 
\text{subject to  }& {{y}_{i}}(\mathbf{w}\cdot {{\mathbf{x}}_{i}}-b)\ge 1- {{\zeta }_{i}} \\ & {{\zeta }_{i}}\ge 0,i=1,...,n \\ 
\end{align}\]
\end{equation}
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}

Thanks, but it throws errors. Does the code compile without errors on your machine? — , Apr 02 '18 at 03:22
Sorry about that, it was totally my fault. And the problem is solved now, thank you very much! — LC Dai, Apr 02 '18 at 03:38

score 2 · Answer 1 · answered Apr 02 '18 at 03:26

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{equation}
    \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}
  \end{equation}
  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 02 '18 at 03:36

Itemization will be interrupted when I insert an equation

1 Answers1