So, a new sample-based classification algorithm, PSS, is proposed. AS, HS and VS algorithms which are parallel algorithms of PSS are presented also.
因此提出一种新的基于抽样的数据分类算法PSS，并提出三种PSS并行化算法AS，HS和VS算法。
The test results show that the PSS algorithm as well as its parallel algorithms are high efficient when they are used in classification of massive database.
实验结果表明，PSS算法及其并行化算法是一种高效的数据分类算法，尤其适用于解决海量数据库中的数据分类问题。
This paper introduces two construction algorithms of Classification decision tree based on parallel algorithm, and analyzes applicability.
本文重点介绍了两种基于并行算法的分类决策树的构造算法，并对它们的适用性及特点作了分析。
This paper designs and implements an engine system of packet classification applying the parallel classifying algorithm, in which network processor is the hardware core.
本文以网络处理器为硬件核心，结合提出的并行包分类算法，设计和实现了一个包分类引擎系统。
Thirdly, a GPU based massively data parallel C-SVM classification (GMP-CSVC) algorithm is presented to reduce the training time of SVM.
第三，针对支持向量机算法复杂度较高，难以应用于大样本分类的问题，提出了GMP-CSVC算法。
Thirdly, a GPU based massively data parallel C-SVM classification (GMP-CSVC) algorithm is presented to reduce the training time of SVM.
第三，针对支持向量机算法复杂度较高，难以应用于大样本分类的问题，提出了GMP-CSVC算法。
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