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== 申请 Applications ==
== 应用 Applications ==
In many applications, one wants to maximize mutual information (thus increasing dependencies), which is often equivalent to minimizing [[conditional entropy]]. Examples include:
In many applications, one wants to maximize mutual information (thus increasing dependencies), which is often equivalent to minimizing [[conditional entropy]]. Examples include:
In many applications, one wants to maximize mutual information (thus increasing dependencies), which is often equivalent to minimizing conditional entropy. Examples include:
In many applications, one wants to maximize mutual information (thus increasing dependencies), which is often equivalent to minimizing conditional entropy. Examples include:
在许多应用程序中，需要最大化互信息（从而增加依赖关系） ，这通常相当于最小化条件熵。例如:
* In [[search engine technology]], mutual information between phrases and contexts is used as a feature for [[k-means clustering]] to discover semantic clusters (concepts).<ref name=magerman>[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.4178&rep=rep1&type=pdf Parsing a Natural Language Using Mutual Information Statistics] by David M. Magerman and Mitchell P. Marcus</ref> For example, the mutual information of a bigram might be calculated as:
* In [[search engine technology]], mutual information between phrases and contexts is used as a feature for [[k-means clustering]] to discover semantic clusters (concepts).<ref name=magerman>[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.4178&rep=rep1&type=pdf Parsing a Natural Language Using Mutual Information Statistics] by David M. Magerman and Mitchell P. Marcus</ref> For example, the mutual information of a bigram might be calculated as:
* Used in Invariant Information Clustering to automatically train neural network classifiers and image segmenters given no labelled data.<ref name=iic>[https://arxiv.org/abs/1807.06653 Invariant Information Clustering for Unsupervised Image Classification and Segmentation] by Xu Ji, Joao Henriques and Andrea Vedaldi</ref>
* Used in Invariant Information Clustering to automatically train neural network classifiers and image segmenters given no labelled data.<ref name=iic>[https://arxiv.org/abs/1807.06653 Invariant Information Clustering for Unsupervised Image Classification and Segmentation] by Xu Ji, Joao Henriques and Andrea Vedaldi</ref>
== 参见 See also ==
== 参见 See also ==