我在“USenglish”设置中使用 bable 包。
我需要避免使用两个字母的连字符。
\lefthyphenmin=3, \righthyphenmin=3 命令在没有 babel 包的情况下也能正常工作。如果使用 babel 包，这两个命令将不起作用。
我将尝试通过以下命令来控制 babel 包连字
\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
\providehyphenmins{english}{\thr@@}
\renewcommand\englishhyphenmins{3}
但这个命令不起作用。
请告知如何通过 babel 包避免两个字母的连字符。
梅威瑟：
\documentclass[USenglish,twocolumn]{article}
\usepackage{babel}
\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
%\providehyphenmins{english}{\thr@@}
%\renewcommand\englishhyphenmins{3}
\lefthyphenmin=3%
\righthyphenmin=3%
\begin{document}
Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph. The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.
In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise, looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.
Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.
Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a ``total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.
In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an ``Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.
\end{document}
请参阅下文，应避免使用连字符连接两个单词。
答案1
您正在使用美国英语，因此您应该重新定义它的连字符：
\documentclass[USenglish,twocolumn]{article}
\usepackage{babel}
\renewcommand\USenglishhyphenmins{33}
\begin{document}
Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph. The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.
In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise, looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.
Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.
Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a ``total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.
In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an ``Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.
\end{document}
\providehyphenmins{USenglish}{33}也可以，但只有前\使用包{babel}。
答案2
\lefthyphenmin=3在\righthyphenmin=3文档环境中写入！
\documentclass[USenglish,twocolumn]{article}
\usepackage{babel}
\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
%\providehyphenmins{english}{\thr@@}
%\renewcommand\englishhyphenmins{3}
\begin{document}
\lefthyphenmin=3
\righthyphenmin=3
Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph. The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.
In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise, looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.
Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.
Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a ``total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.
In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an ``Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.
\end{document}