我正在使用部门提供的模板，其中包含 thesis.tex 文件，我认为其相关内容在这里
\documentclass[twoside,mtp]{iiitg}
\usepackage{fancyhdr}
\fancyhead{}
\fancyhead[LO]{\slshape \rightmark}
\fancyhead[RO,LE]{\textbf{\thepage}}
\fancyhead[RE]{\slshape \leftmark}
\fancyfoot{}
\pagestyle{fancy}
\renewcommand{\chaptermark}[1]{\markboth{\chaptername \ \thechapter \ \ #1}{}}
\renewcommand{\sectionmark}[1]{\markright{\thesection \ \ #1}}
\tableofcontents
\clearemptydoublepage
% Make the list of figures
\listoffigures
\clearemptydoublepage
% Make the list of tables
\listoftables
\clearemptydoublepage
%\phantomsection \addcontentsline{toc}{chapter}{List of Symbols and Abbreviation}
%\include{files/symb_b}
%\include{files/symb_b1}
%\clearemptydoublepage
\onehalfspace
% Start regular page counting at page 1
\mainmatter
\addtolength{\parskip}{0.05\baselineskip}
\abovedisplayskip=13pt
\belowdisplayskip=13pt
\clearemptydoublepage
\input{texfiles/chapter1}
\clearemptydoublepage
\input{texfiles/chapter2}
\clearemptydoublepage
\input{texfiles/chapter3}
\clearemptydoublepage
\input{texfiles/chapter4}
\clearemptydoublepage
\input{texfiles/conclusion}
\clearemptydoublepage
在第 2 章中，我制作了一个很长的表格，由于上面的文字，它不适合放在一页中。所以我按照以下方式编写了它。
\begin{xltabular}{\textwidth}{@{} l X @{} X @{} X}
\hline
\thead{Algorithm} & \thead{Pros} & \thead{Cons} \\ \hline
\begin{enumerate}[label={}, wide = 0pt, leftmargin = *, nosep, itemsep = 0pt, before = \vspace*{\baselineskip}, after =\vspace*{\baselineskip} ]
\item K Nearest Neighbour
\item K-NN
\end{enumerate} & \begin{enumerate}
\item Very easy to understand
\item Good for creating models that include non standard data types such as
text
\end{enumerate} & Large Storage requirements
Computationally Expensive
Sensitive to the choice of the similarity function for comparing instances \\ \hline
Local Outlier Factor(LOF) & Well-known and good algorithm
for local anomaly detection
& Only relies on its direct neighborhood .\newline Perform poorly on data sets with global anomalies. \\ \hline
K Means & Low Complexity \newline Very easy to implement & Each cluster has pretty equal number of observations \newline Necessity of specifying K \newline Only work with numerical data \\ \hline
Support Vector Machine (SVM) & Find the best separation hyper-plane.Deal with very high dimensional data.\newline
Can learn very elaborate concepts.
Work very well & Require both positive and negative examples. Require lots of memory.\newline Some numerical stability problems.Need to select a good kernel function \\ \hline
Neural networks based anomaly detection & Learns and does not need to be reprogrammed.\newline Can be implemented in any application & Needs training to operate \newline Requires high processing time for large neural networks \newline The architecture needs to be emulated \\ \hline
\caption{Anomaly Detection Algorithms comparison}
\label{tab:algorithm_comp}
\end{xltabular}
生成的表格如下所示
我认为这是有错误的。从外观上看，我不清楚错误是什么。上述实现中的错误可能是什么？
答案1
主要问题是l您尝试放置多个段落的列。我将其重新定义为左对齐的 x 列。如果您需要不同的列宽，请查看有关使用的 tabularx-documentation \hsize。
我还定义了缺少的\thead命令，并将所有\hline命令更改为 booktabs-rules。
这是一些糟糕的换行符，但可以修复。
\documentclass{article}
\usepackage{xltabular, booktabs, enumitem}
\usepackage{babel}
\newcommand{\thead}[1]{\multicolumn{1}{c}{\bfseries #1}}
\begin{document}
\begin{xltabular}{\textwidth}{@{} >{\raggedright\arraybackslash}X X X @{}}
\caption{Anomaly Detection Algorithms comparison\label{tab:algorithm_comp}}\\
\toprule
\thead{Algorithm} & \thead{Pros} & \thead{Cons} \\ \midrule
\begin{enumerate}%
[label={}, wide = 0pt, leftmargin = *, nosep, itemsep = 0pt, before = \vspace*{\baselineskip}, after =\vspace*{\baselineskip} ]
\item K Nearest Neighbour
\item K-NN
\end{enumerate} & \begin{enumerate}
\item Very easy to understand
\item Good for creating models that include non standard data types such as
text
\end{enumerate} & Large Storage requirements
Computationally Expensive
Sensitive to the choice of the similarity function for comparing instances \\ \midrule
Local Outlier Factor(LOF) & Well-known and good algorithm
for local anomaly detection
& Only relies on its direct neighborhood .\newline Perform poorly on data sets with global anomalies. \\ \midrule
K Means & Low Complexity \newline Very easy to implement & Each cluster has pretty equal number of observations \newline Necessity of specifying K \newline Only work with numerical data \\ \midrule
Support Vector Machine (SVM) & Find the best separation hyper-plane.Deal with very high dimensional data.\newline
Can learn very elaborate concepts.
Work very well & Require both positive and negative examples. Require lots of memory.\newline Some numerical stability problems.Need to select a good kernel function \\ \midrule
Neural networks based anomaly detection & Learns and does not need to be reprogrammed.\newline Can be implemented in any application & Needs training to operate \newline Requires high processing time for large neural networks \newline The architecture needs to be emulated \\ \bottomrule
\end{xltabular}
\end{document}
答案2
这是我的建议。我已将列中的水平对齐方式从两端对齐更改为左对齐，以防止单词之间出现较大的空白。由于第一列包含的文本比第二列和第三列少，因此我相对于其他列减小了第一列的列宽。为了提供更多结构，我tabitem为“优点”和“缺点”列中的条目使用了新定义的环境。在下面的 MWE 中，我还包含了另一个只需要两列的示例：
\documentclass{article}
\usepackage{xltabular, booktabs, enumitem}
\newlist{tabitem}{itemize}{1}
\setlist[tabitem]{wide=0pt, nosep, leftmargin= * ,label=\textendash,after=\vspace{-\baselineskip},before=\vspace{-0.6\baselineskip}}
\usepackage{makecell}
\renewcommand{\theadfont}{\normalsize\bfseries}
\newcolumntype{L}{>{\raggedright\arraybackslash}X}
\begin{document}
\begin{xltabular}{\textwidth}{@{} >{\raggedright\arraybackslash}p{1.85cm}LL @{}}
\caption{Anomaly Detection Algorithms comparison\label{tab:algorithm_comp}}\\
\toprule
\thead{Algorithm} & \thead{Pros} & \thead{Cons} \\
\midrule
\endfirsthead
\toprule
\thead{Algorithm} & \thead{Pros} & \thead{Cons} \\
\midrule
\endhead
K Nearest Neighbour K-NN
& \begin{tabitem}
\item Very easy to understand
\item Good for creating models that include non standard data types such as text
\end{tabitem}
& \begin{tabitem}
\item Large Storage requirements
\item Computationally Expensive
\item Sensitive to the choice of the similarity function for comparing instances
\end{tabitem} \\
\midrule
Local Outlier Factor (LOF)
& \begin{tabitem}
\item Well-known and good algorithm for local anomaly detection
\end{tabitem}
& \begin{tabitem}
\item Only relies on its direct neighborhood.
\item Perform poorly on data sets with global anomalies.
\end{tabitem}\\
\midrule
K Means
& \begin{tabitem}
\item Low Complexity
\item Very easy to implement
\end{tabitem}
& \begin{tabitem}
\item Each cluster has pretty equal number of observations
\item Necessity of specifying K
\item Only work with numerical data
\end{tabitem} \\
\midrule
Support Vector Machine (SVM)
& \begin{tabitem}
\item Find the best separation hyper-plane.
\item Deal with very high dimensional data.
\item Can learn very elaborate concepts.
\item Work very well
\end{tabitem}
& \begin{tabitem}
\item Require both positive and negative examples.
\item Require lots of memory.
\item Some numerical stability problems.
\item Need to select a good kernel function
\end{tabitem} \\
\midrule
Neural networks based anomaly detection
& \begin{tabitem}
\item Learns and does not need to be reprogrammed
\item Can be implemented in any application
\end{tabitem}
& \begin{tabitem}
\item Needs training to operate
\item Requires high processing time for large neural networks
\item The architecture needs to be emulated
\end{tabitem}\\
\bottomrule
\end{xltabular}
%\pagebreak
\begin{xltabular}{\textwidth}{LL @{}}
\caption{Anomaly Detection Algorithms comparison\label{tab:algorithm_comp}}\\
\toprule
Pros & Cons \\
\midrule
\endfirsthead
\toprule
\thead{Pros} & \thead{Cons} \\
\midrule
\endhead
\multicolumn{2}{@{}l}{\itshape K Nearest Neighbour K-NN}\\*
\begin{tabitem}
\item Very easy to understand
\item Good for creating models that include non standard data types such as text
\end{tabitem}
& \begin{tabitem}
\item Large Storage requirements
\item Computationally Expensive
\item Sensitive to the choice of the similarity function for comparing instances
\end{tabitem} \\
\midrule
\multicolumn{2}{@{}l}{\itshape Local Outlier Factor (LOF)}\\*
\begin{tabitem}
\item Well-known and good algorithm for local anomaly detection
\end{tabitem}
& \begin{tabitem}
\item Only relies on its direct neighborhood.
\item Perform poorly on data sets with global anomalies.
\end{tabitem}\\
\midrule
\multicolumn{2}{@{}l}{\itshape K Means}\\*
\begin{tabitem}
\item Low Complexity
\item Very easy to implement
\end{tabitem}
& \begin{tabitem}
\item Each cluster has pretty equal number of observations
\item Necessity of specifying K
\item Only work with numerical data
\end{tabitem} \\
\midrule
\multicolumn{2}{@{}l}{\itshape Support Vector Machine (SVM)}\\*
\begin{tabitem}
\item Find the best separation hyper-plane.
\item Deal with very high dimensional data.
\item Can learn very elaborate concepts.
\item Work very well
\end{tabitem}
& \begin{tabitem}
\item Require both positive and negative examples.
\item Require lots of memory.
\item Some numerical stability problems.
\item Need to select a good kernel function
\end{tabitem} \\
\midrule
\multicolumn{2}{@{}l}{\itshape Neural networks based anomaly detection}\\
\begin{tabitem}
\item Learns and does not need to be reprogrammed
\item Can be implemented in any application
\end{tabitem}
& \begin{tabitem}
\item Needs training to operate
\item Requires high processing time for large neural networks
\item The architecture needs to be emulated
\end{tabitem}\\
\bottomrule
\end{xltabular}
\end{document}
一种完全不同的方法：
\documentclass{article}
\usepackage{enumitem}
\newlist{proconlist}{itemize}{1}
\setlist[proconlist]{label=+.,leftmargin=*, nosep}
\usepackage{caption}
\begin{document}
\captionof{table}{Pros (+) and Cons (--) of Different Anomaly Detection Algorithms \label{tab:algorithm_comp}}
\begin{enumerate}[leftmargin=*]
\item K Nearest Neighbour K-NN
\begin{proconlist}
\item[+] Very easy to understand
\item[+] Good for creating models that include non standard data types such as text
\item[--] Large Storage requirements
\item[--] Computationally Expensive
\item[--] Sensitive to the choice of the similarity function for comparing instances
\end{proconlist}
\item Local Outlier Factor (LOF)
\begin{proconlist}
\item[+] Well-known and good algorithm for local anomaly detection
\item[--] Only relies on its direct neighborhood.
\item[--] Perform poorly on data sets with global anomalies.
\end{proconlist}
\item K Means
\begin{proconlist}
\item[+] Low Complexity
\item[+] Very easy to implement
\item[--] Each cluster has pretty equal number of observations
\item[--] Necessity of specifying K
\item[--] Only work with numerical data
\end{proconlist}
\item Support Vector Machine (SVM)
\begin{proconlist}
\item[+] Find the best separation hyper-plane.
\item[+] Deal with very high dimensional data.
\item[+] Can learn very elaborate concepts.
\item[+] Work very well
\item[--] Require both positive and negative examples.
\item[--] Require lots of memory.
\item[--] Some numerical stability problems.
\item[--] Need to select a good kernel function
\end{proconlist}
\item Neural networks based anomaly detection
\begin{proconlist}
\item[+] Learns and does not need to be reprogrammed
\item[+] Can be implemented in any application
\item[--] Needs training to operate
\item[--] Requires high processing time for large neural networks
\item[--] The architecture needs to be emulated
\end{proconlist}
\end{enumerate}
\end{document}