We adopt a way of attribute selection based on word entropy, use vectors which are represented by word frequency, and deduce its corresponding Bayesian formula.
我们采用了基于词熵的特征项提取方法，并且使用特征项单词出现频率来表示向量，推导出相应的贝叶斯计算公式。
In the paper, we use the statistical theory to calculate the probability of video semantics by Bayesian formula, choose the semantic of maximal probability to label the unlabeled samples.
文中采用统计学理论，利用贝叶斯概率公式计算视频语义出现的概率，选取概率最大的类别标注未标记的样本。
Firstly, a kind of abbreviated forecasting formula was proposed after the mathematical basis of Bayesian network had been depicted.
首先阐述了贝叶斯网络的数学描述，在此基础上给出贝叶斯网络预测公式的一种简化形式。
Then a Bayesian network model is built for all these queries, and the probability formula of each structured query given the document collection is inferred in the model.
对这些查询建立贝叶斯网络模型，通过模型推导出各个查询在当前文档集合下的概率公式。
Then a Bayesian network model is built for all these queries, and the probability formula of each structured query given the document collection is inferred in the model.
对这些查询建立贝叶斯网络模型，通过模型推导出各个查询在当前文档集合下的概率公式。
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