An algorithm based on rough set theory for extracting rules from data class by class is proposed.
提出一种基于粗糙集理论的分类别进行规则抽取的算法。
For extracting rules from binary neural networks, it is important to analyze logical meaning of neurons.
剖析二进神经元的逻辑意义对二进神经网络的规则提取是十分重要的。
An extracting rules algorithm based on class feature matrix is presented, which has made use of the characteristic of discernibility matrix.
充分考虑了可辨识矩阵的特性，提出一种基于类别特征矩阵的决策规则提取算法。
Since extracting rules from support vector machines help to solve those problems, this area is becoming a hot topic in both machine learning and intelligent computing communities.
由于对支持向量机规则提取进行研究有助于解决上述问题，因此该领域正成为机器学习和智能计算界的研究热点。
Since extracting rules from support vector machines help to solve those problems, this area is becoming a hot topic in both machine learning and intelligent computing communities.
由于对支持向量机规则提取进行研究有助于解决上述问题，因此该领域正成为机器学习和智能计算界的研究热点。
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