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。
finally, the experiment process is introduced, comparing with the binary decision tree based on minimum entropy heuristic information, the results show the algorithm proposed in this paper is valid.
最后,给出了实验过程,与用熵作启发式的二叉决策树的比较结果表明了本文算法的有效性。
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.