欧阳柏成. 网络大数据下的冗余数据分类优化算法研究[J]. 微电子学与计算机, 2015, 32(1): 128-130.
引用本文: 欧阳柏成. 网络大数据下的冗余数据分类优化算法研究[J]. 微电子学与计算机, 2015, 32(1): 128-130.
OUYANG Bo-cheng. Research on the Redundancy Data Classification Optimization Algorithm under the Big Data of Network[J]. Microelectronics & Computer, 2015, 32(1): 128-130.
Citation: OUYANG Bo-cheng. Research on the Redundancy Data Classification Optimization Algorithm under the Big Data of Network[J]. Microelectronics & Computer, 2015, 32(1): 128-130.

网络大数据下的冗余数据分类优化算法研究

Research on the Redundancy Data Classification Optimization Algorithm under the Big Data of Network

  • 摘要: 提出了基于模糊支持向量机算法网络大数据下的冗余数据分类优化方法。提取网络大数据环境下的冗余数据属性特征,为冗余数据分类提供准确的数据基础。根据模糊支持向量机相关理论,获取最优分类平面,从而实现冗余数据分类优化处理。实验结果表明,利用改进算法进行网络大数据下的冗余数据分类优化处理,能够提高分类的准确性,取得了令人满意的效果。

     

    Abstract: The fuzzy support vector machine (SVM) algorithm is proposed under the large data of network redundancy data classification optimization method. Extract the network under the environment of large data redundancy data attributes, providing accurate data basis for redundant data classification. According to the related theory, fuzzy support vector machine (SVM) to obtain the optimal classification plane, so as to realize optimization of redundant data classification. The experimental results show that the improved algorithm under the large data of network optimization of redundant data classification, can improve the accuracy of classification, satisfactory results have been achieved.

     

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