The choice of attribute selection metric to split has an important impact on the shape and the depth of the resulting decision tree.
在根据入侵规则构造决策树时，所依据的分类属性选择标准对决策树的形状和深度有很大的影响。
The abstract channel model for learning from examples is presented and a new attribute selection measure (channel capacity) is introduced.
本文提出了示例学习的抽象信道模型，引入一个新的特征选择量——信道容量。
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。
The decision tree constructed with the new standard of attribute selection has the following characteristics: fewer leaf nodes, fewer levels of average depth, better generalization of leaf nodes.
用新的属性选择标准生成的决策树一般具有叶子数目较少，叶子的平均深度也较小，且叶子具有较强的泛化能力。
The former algorithm makes improvements from three aspects: discretization, reducing dimension, attribute selection, which effectively solves the conflict between efficiency and prediction precision.
前者从数据的离散化，降维，和属性选择方面有效的解决了处理大规模高维数据库时的效率与精度之间的矛盾。
The former algorithm makes improvements from three aspects: discretization, reducing dimension, attribute selection, which effectively solves the conflict between efficiency and prediction precision.
前者从数据的离散化，降维，和属性选择方面有效的解决了处理大规模高维数据库时的效率与精度之间的矛盾。
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