A hierarchical decomposed support vector machines binary decision tree is used for classification.
采用一种层次分解的支持向量机二叉决策树进行分类识别。
Gaussian similarity is used for measuring the distance of different covariance. A binary decision tree is constructed with this measure.
该算法是用高斯相似度度量协方差矩阵间的距离，并由此测度建立了反映协方差矩阵结构关系的二叉决策树。
At last, a binary decision tree could be built. Algorithm analysis and simulation results show that RMBRDM can support rules with ranges and the performance of RMBRDM is better than that of PTS.
最后建立一棵二叉决策树。理论分析和仿真实验均表明，RMBRDM算法不仅支持以范围形式表示的规则，且时空性能优于PTS算法。
At last, a binary decision tree could be built. Algorithm analysis and simulation results show that RMBRDM can support rules with ranges and the performance of RMBRDM is better than that of PTS.
最后建立一棵二叉决策树。理论分析和仿真实验均表明，RMBRDM算法不仅支持以范围形式表示的规则，且时空性能优于PTS算法。
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