[1]厍向阳,崔文强.改进的并行SVM回归算法[J].西安科技大学学报,2017,(02):299-304.[doi:10.13800/j.cnki.xakjdxxb.2017.0223]
 SHE Xiang-yang,CUI Wen-qiang.Impoved parallel SVM regression algorithm[J].Journal of Xi’an University of Science and Technology,2017,(02):299-304.[doi:10.13800/j.cnki.xakjdxxb.2017.0223]
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改进的并行SVM回归算法(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2017年02期
页码:
299-304
栏目:
出版日期:
2017-03-30

文章信息/Info

Title:
Impoved parallel SVM regression algorithm
文章编号:
1672-9315(2017)02-0299-06
作者:
厍向阳崔文强
西安科技大学 计算机科学与技术学院,陕西 西安 710054
Author(s):
SHE Xiang-yangCUI Wen-qiang
College of Computer Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,China
关键词:
支持向量机 K-means聚类 并行计算 顺次最小优化算法
Keywords:
Key words:support vector machine K-means clustering parallel computing sequential minimal optimization algorithm
分类号:
TP 312
DOI:
10.13800/j.cnki.xakjdxxb.2017.0223
文献标志码:
A
摘要:
针对目前SVM回归算法在大样本情况下,学习效率低、精度不高的问题,提出了基于K-means聚类的并行SVM回归算法。在Hadoop框架中,先对训练样本行进并行聚类,然后针对聚类后的不同簇,构造相应的SVM回归模型,使用顺次最小优化算法求解各模型参数。预测时,选择与待预测样本距离最近簇的对应SVM回归模型进行预测。实验验证了文中算法的可行性和有效性。
Abstract:
Abstract:Aiming at the low efficiency and poor accuracy problem of serial traditional SVM regression algorithm in large sample cases,the improved parallel SVM regression algorithm based on K-means clustering is proposed.In the framework of Hadoop,the training sample is clustered parallelly,then the SVM regression model is constructed for every cluster,the parameters of the SVM regression model are solved by sequential minimal optimization algorithm.The cluster regression model of the nearest cluster to test sample is selected to predict.Algorithm testing show:Impoved parallel SVM regression algorithm is feasible andeffective.

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备注/Memo

备注/Memo:
基金项目:陕西省教育厅专项科研计划项目(12JK0787)通讯作者:厍向阳(1968-),男,陕西周至人,博士后,教授,E-mail:xiangyangshe@sohu.com
更新日期/Last Update: 1900-01-01