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。
Rough Set, as one of the Granular computing's three main models, have attracted much attention since its 'born and has a broad application on machine learning, pattern recognition and other fields.
粗糙集理论作为粒度计算的三大模型之一，自产生起就备受关注，并已广泛应用与机器学习、模式识别等领域。
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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