[1]赵新苗,冯向萍,赵涛. 农业信息分类中K-means与SVM的混合算法研究[J].计算机技术与发展,2017,27(06):178-182.
　ZHAO Xin-miao,FENG Xiang-ping,ZHAO Tao. Investigation on K-means and SVM Mixed Algorithm for Agriculture Information Classification[J].,2017,27(06):178-182.
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 农业信息分类中K-means与SVM的混合算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年06期
页码:
178-182
栏目:
应用开发研究
出版日期:
2017-06-10

文章信息/Info

Title:
 Investigation on K-means and SVM Mixed Algorithm for Agriculture Information Classification
文章编号:
1673-629X（2017)06-0178-05
作者:
 赵新苗; 冯向萍; 赵涛
 新疆农业大学 计算机与信息工程学院
Author(s):
 ZHAO Xin-miao; FENG Xiang-ping; ZHAO Tao
关键词:
 ; 农业信息; 分类; 聚类; 边缘样本删减
Keywords:
 ; agricultural information; classification; clustering; edge samples reduction
分类号:
TP301.6
文献标志码:
A
摘要:
 随着新疆农业信息技术的不断发展和农村互联网的广泛普及,互联网中海量的农业相关知识和信息虽然为工作人员带来了便利,但是与此同时也给信息检索增加了难度.在对具有新疆特色的农作物网页信息分类研究的基础上,提出并实现了K-means与SVM相结合的分类方法,以帮助农业相关工作人员获得更准确有效的信息.该分类方法采用K-means对训练样本进行聚类以减少边缘训练样本,并应用SVM对删减后的训练集进行训练.为减少训练集边缘样本、节省训练时间,还提出了两种基于中心向量的边缘样本删减方法,分别仅保留中心向量方法和保留中心向量临近样本.实验验证结果表明,所提出的方法均能够同时有效地减少训练样本和训练时间.
Abstract:
 With the continuous development of Xinjiang agricultural information technology and the widespread popularity of rural Internet,the amount of relevant knowledge and information in Internet has been bringing lots of conveniences for people and difficulty for effective information retrieval at the same time.Based on the requirement analysis of Xinjiang Rural Information Acquisition System and aiming at categorization of the web pages which are about characteristic crops in Xinjiang to help display more accurate and effective agricultural information and reduce the number of training sets and save training time,a method combined with SVM and K-means has been proposed.Its main process contains clustering the training sets with K-means to delete edge samples and training the SVM on the new deleted training sets.Two methods of deleting edge samples and retaining neighbors of the centers have also been proposed.Experimental results show that these methods can decrease training samples and training time.

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更新日期/Last Update: 2017-07-28