This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
Decision tree, as a flow chart, is structure of a tree, which is mostly used in finding classification rules and prediction of classification.
决策树是一个类似于流程图的树结构，主要用途是提取分类规则，进行分类预测。
The introduction of generalized decision tree(GDT) realized the unification of classification rules and decision tree structure.
文章引入了广义决策树的概念，实现了分类规则集和决策树结构的统一。
Now consider how a decision tree model can be built of the decision structure visualized in the influence diagram.
那么决策树模型又是如何根据可视化影响图网中的结构逐渐形成的呢？
The structure contains all the decision tree training parameters.
该结构包含了所有的决策树训练所需的参数。
CART decision tree has outstanding advantages on the issue of classification in dealing with multi-dimensional data with complex structure.
决策树能实现快速分类，CART决策树在处理复杂结构的多维数据分类问题上具有突出优势。
CART decision tree has outstanding advantages on the issue of classification in dealing with multi-dimensional data with complex structure.
决策树能实现快速分类，CART决策树在处理复杂结构的多维数据分类问题上具有突出优势。
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