我正在使用 overleaf 格式化带有 BibTex 的 IEEEtran 文档类的论文。但是，我想减少参考条目内行之间的行距（而不是条目本身之间的行距）。
我在网上看到，我们可以使用 setspace 包并设置间距，然后使用“\printbibliography”命令来减少间距。但是，一旦我在页面顶部包含 setspace 包，我就会发现我的论文中的表格间距发生了变化。
建议我采取一些措施来解决这个问题。
德克萨斯州。
代码：
\documentclass[journal,comsoc]{IEEEtran}
\usepackage{commath}
\usepackage{setspace}
\usepackage{biblatex}
\bibliographystyle{IEEEtran}
\bibliography{reference}
\begin{document}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{who2020,mirtchouk2017recognizing} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\begin{table}[!htp]
\vspace{-4mm}
\caption{SBL - Model Summaries}
\label{tab:popular_sparse_bayesian_models}
\begin{threeparttable}
\centering
\begin{tabular}{p{0.5cm}p{1.5cm}p{2cm}p{2.5cm}}
\hline\hline
& & & \\
\hline
\textbf{Ref.} & \textbf{Method} & \textbf{Model} & \textbf{Priors} \\ [1.0ex]
\hline
RVMs\\
\hline
\rule{0pt}{8pt}\cite{mirtchouk2017recognizing} & RVM & Sigmoid & Zero-mean Gaussian \\
\rule{0pt}{8pt}\cite{mirtchouk2017recognizing} & Fast RVM & Sigmoid & Zero-mean Gaussian \\
\hline
\end{tabular}
\end{threeparttable}
\end{table}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{who2020,mirtchouk2017recognizing} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{who2020,mirtchouk2017recognizing} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\ifCLASSOPTIONcaptionsoff
\newpage
\fi
\printbibliography
\end{document}
答案1
如果您正在使用该类，IEEEtran那么它大概是为了在 IEEE 上发表。在这种情况下，我认为尝试弄乱文档中的间距不是一个好主意：类中的设置是有原因的，并且根据发布工作流程，您的更改甚至可能不会出现在已发表的论文中。
无论如何，问题是由setspace和之间的“不兼容”引起的IEEEtran。IEEEtran将表格切换为较小的字体，并使用
\def\table{\def\@floatboxreset{\reset@font\footnotesize\@setminipage}\@float{table}}
并setspace重置所有浮动元素的行距
\let\latex@xfloat=\@xfloat
\def\@xfloat #1[#2]{%
\latex@xfloat #1[#2]%
\def\baselinestretch{\setspace@singlespace}%
\normalsize
}
但setspace的定义明确地设置了\normalsize覆盖IEEEtran的\footnotesize。
这是解决这个问题的一种方法
\documentclass{IEEEtran}
\usepackage{setspace}
\makeatletter
\let\@xfloat\latex@xfloat
\def\table{%
\def\@floatboxreset{%
\reset@font\def\baselinestretch{\setspace@singlespace}\footnotesize\@setminipage}%
\@float{table}}
\@namedef{table*}{%
\def\@floatboxreset{%
\reset@font\def\baselinestretch{\setspace@singlespace}\footnotesize\@setminipage}%
\@dblfloat{table}}
\makeatother
\begin{document}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\begin{table}[!htp]
\vspace{-4mm}
\caption{SBL - Model Summaries}
\label{tab:popular_sparse_bayesian_models}
%\begin{table}
\centering
\begin{tabular}{p{0.5cm}p{1.5cm}p{2cm}p{2.5cm}}
\hline\hline
& & & \\
\hline
\textbf{Ref.} & \textbf{Method} & \textbf{Model} & \textbf{Priors} \\ [1.0ex]
\hline
RVMs\\
\hline
\rule{0pt}{8pt}\cite{article-full} & RVM & Sigmoid & Zero-mean Gaussian \\
\rule{0pt}{8pt}\cite{article-full} & Fast RVM & Sigmoid & Zero-mean Gaussian \\
\hline
\end{tabular}
%\end{threeparttable}
\end{table}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\bibliographystyle{IEEEtran}
\bibliography{xampl}
\end{document}
我们禁用setspace的补丁，并自行将间距补丁插入到table环境中。
如果您不更改文档间距，而仅计划对setspace没有浮动的文档部分使用环境，则以下较短的修复方法就足够了。
\documentclass{IEEEtran}
\usepackage{setspace}
\makeatletter
\let\@xfloat\latex@xfloat
\makeatother
\begin{document}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\begin{table}[!htp]
\vspace{-4mm}
\caption{SBL - Model Summaries}
\label{tab:popular_sparse_bayesian_models}
%\begin{table}
\centering
\begin{tabular}{p{0.5cm}p{1.5cm}p{2cm}p{2.5cm}}
\hline\hline
& & & \\
\hline
\textbf{Ref.} & \textbf{Method} & \textbf{Model} & \textbf{Priors} \\ [1.0ex]
\hline
RVMs\\
\hline
\rule{0pt}{8pt}\cite{article-full} & RVM & Sigmoid & Zero-mean Gaussian \\
\rule{0pt}{8pt}\cite{article-full} & Fast RVM & Sigmoid & Zero-mean Gaussian \\
\hline
\end{tabular}
%\end{threeparttable}
\end{table}
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
jointly encourages sparsity to features and samples. However, in order to select features for non-linear basis functions, we have to differentiate, which leads to high computational costs. As future work, we plan to use incremental learning \cite{article-full,incollection-full} to reduce the computational costs. We also plan to design an online strategy [43] for joint feature and classifier learning. Also, PFCVMLP focuses on the supervised binary classification. It would be interesting to extend PFCVMLP to solve multi-class problems [24, 41] and semi-supervised form [22]. Finally, we aim to use PFCVMLP in other areas of research, such as in bioinformatics problems and clinical diagnoses
\bibliographystyle{IEEEtran}
\bibliography{xampl}
\end{document}