As an effect tool of pattern recognition and data processing, rough set theory (RST) and support vector machine (SVM) have become the focus of research in machine learning.

粗糙集理论(rst)与支持向量机(SVM)作为模式识别，数据处理的有效工具，已成为机器学习的研究热点。

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Explores the training problems of support vector machine with large training pattern set, and a new parallel algorithm based on orthogonal array is presented.

对大规模训练样本的支持向量机训练问题进行探索，提出了一种基于正交表的并行学习算法。

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Similarly, based on rough set theory to feature-set reduction, in the optimal decision based on the use of the property least squares support vector machine classifier to identify the flow pattern.

同样，基于粗糙集理论对特征集进行约简，在最优决策属性的基础上使用最小二乘支持向量机分类器对流型进行识别。

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