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。

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The uncertainty coefficient is an information measure for a feature subset, and it is monotonic, so it can be taken as the feature selection measure.

特征子集的不确定性系数是一种单调的特征子集信息度量，因此可以将它作为特征选择度量。

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For decreasing calculation complexity and increasing speed of algorithm learning, it presented some improvement of data discretization and attributes selection measure.

对数据离散化及属性选择度量方法作了改进，以降低计算复杂度，提高算法学习速度。

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