我有一个比我的文档的\ that宽度更宽的表格。(我不想更改\文本宽度。)我想通过换行最后一列(最右边的列)的文本来强制表格适应\textwidth。这是我的表的代码:
\begin{table*}[!t]
\caption{Extracted Features used in Meta-Learning} \label{sample-features}
\label{tbl:features}
\begin{center}
\begin{tabular*}{0.95\textwidth,center}{|l||l|}
{\textbf{FEATURE}} &{\textbf{DESCRIPTION}} \\
\hline \\
n.examples & Number of examples \\
n.attrs & Number of attributes \\
prop.symbolic.attrs & Proportion of symbolic attributes \\
prop.missing.values & Proportion of missing values \\
class.entropy & Class entropy \\
avg.mutual.information & Average mutual information \\
prop.h.outlier & Proportion of continuous attributes with outliers \\
avg.attr.entropy & Average attribute entropy \\
avg.symb.pair.mutual.infor & Average mutual information between pairs of symbolic attributes \\
avg.abs.attr.correlation & Average absolute correlation between continuous attributes \\
avg.skewness & Mean skewness of attributes \\
avg.abs.skewness & Mean absolute skewness of attributes \\
avg.kurtosis & Mean kurtosis of attributes \\
canonical.correlation.best.linear.combination & Canonical correlation of the best linear combination of attributes to distinguish between classes \\
relative.prop.best.linear.combination & Proportion of the total discrimination power explained by the best linear combination \\
\end{tabular*}
\end{center}
\end{table*}此代码的结果为以下文档:

红线右边的部分(由我自己添加)应该是换行的。
有什么帮助吗?
发布于 2015-03-30 22:34:50
这是一个答案。我希望它能帮助你:
使用package tabularx的方法如下:
\usepackage{tabularx,booktabs}
.
.
.
\begin{table*}[!t]
\caption{Caption}
\label{tbl:tbl}
\centering
\begin{tabularx}{\textwidth}{X X}
\toprule
Item 1 & Item 2 \\
\midrule
Item 1 description & Item 2 description \\
.
.
.
\bottomrule
\end{tabularx}
\end{table*}在我的例子中,解决方案如下:
\begin{table*}[!t]
\caption{Extracted Features used in Meta-Learning} \label{sample-features}
\label{tbl:features}
\centering
\begin{tabularx}{0.95\textwidth}{|X||X|}
\toprule
{\textbf{FEATURE}} &{\textbf{DESCRIPTION}} \\
\midrule
n.examples & Number of examples \\
n.attrs & Number of attributes \\
prop.symbolic.attrs & Proportion of symbolic attributes \\
prop.missing.values & Proportion of missing values \\
class.entropy & Class entropy \\
avg.mutual.information & Average mutual information \\
prop.h.outlier & Proportion of continuous attributes with outliers \\
avg.attr.entropy & Average attribute entropy \\
avg.symb.pair.mutual.infor & Average mutual information between pairs of symbolic attributes \\
avg.abs.attr.correlation & Average absolute correlation between continuous attributes \\
avg.skewness & Mean skewness of attributes \\
avg.abs.skewness & Mean absolute skewness of attributes \\
avg.kurtosis & Mean kurtosis of attributes \\
canonical.correlation.best.linear.combination & Canonical correlation of the best linear combination of attributes to distinguish between classes \\
relative.prop.best.linear.combination & Proportion of the total discrimination power explained by the best linear combination \\
\bottomrule
\end{tabularx}
\end{table*}https://stackoverflow.com/questions/29348517
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