计算机科学 ›› 2006, Vol. 33 ›› Issue (4): 145-147.

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基于聚类的大样本支持向量机研究

  

  • 出版日期:2018-11-17 发布日期:2018-11-17
  • 基金资助:
    国家自然科学基金资助项目(基金编号:10371135).

  • Online:2018-11-17 Published:2018-11-17

摘要: 针对大样本支持向量机内存开销大、训练速度慢的缺点,本文提出了基于聚类支持向量机,运用k-mean对样本聚类,压缩样本量,构造初始超平面,筛选出靠近超平面的支持粪和可能支持向量,重新构造决策超平面。实验表明,在保持泛化精度基本一致前提下,改进算法的训练速度明显提高。

关键词: 支持向量机分类 大样本 聚类

Abstract: Training a support vector machines on a data set of huge size exists one problem with stow training process. In this paper,we use a modified support vector machines clustering-based support vector machines to resolve this problent It speeds up the trainin

Key words: Support vector machine classification, Large-scale samples, k-mean Clustering

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