[1]江伟,潘昊.基于优化的多核学习方法的Web文本分类的研究[J].计算机技术与发展,2013,(10):80-82.
 JIANG Wei[],PAN Hao[].Research of Web Document Classification Based on Optimized Multiple Kernel Learning Method[J].,2013,(10):80-82.
点击复制

基于优化的多核学习方法的Web文本分类的研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年10期
页码:
80-82
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Research of Web Document Classification Based on Optimized Multiple Kernel Learning Method
文章编号:
1673-629X(2013)10-0080-03
作者:
江伟12潘昊1
[1]武汉理工大学 计算机科学与技术学院;[2]武汉科技大学城市学院 信息工程学部
Author(s):
JIANG Wei[12]PAN Hao[1]
关键词:
支持向量机数据挖掘多核学习Web文本分类
Keywords:
SVMdata miningmultiple kernel learningWeb document classification
文献标志码:
A
摘要:
Web文本分类技术是数据挖掘中一个重要研究领域,为了能从海量信息中快速检索遍布网络各处的文档,需要提高Web文本分类技术的性能。多核学习方法是当前机器学习领域的一个热点,可以显著提升分类识别能力和学习推广能力,而核方法是解决高维非线性模式分析的有效方法之一。利用多核代替单核能增强决策函数的可解释性并获得更优的性能。文中分析研究了一种基于优化的多核学习的支持向量机,在此基础上结合通用的Web文本分类模型,提出了一种基于多核学习支持向量机的Web分类方法。通过实验测试表明,该方法具有良好的效果,对比一致组合的多核学习方法,所提出的方法具有较高的准确率
Abstract:
Web document classification has been considered as an important research field in data mining,it's necessary to improve the performance of technique of Web document classification for quickly retrieving the documents from the massive information spread all o-ver the network. Multiple-kernel learning is a focus in current machine learning community,which is able to develop the capability of classification and learning extension,while kernel method is one of effective approaches for solving high dimension and non-linear pattern analysis. By using the advantage of multiple kernel can boost interpretability of decision function and obtain better performance. In this paper,propose a Web document classification based on multiple kernel learning after a research of a SVM based on multiple kernel learn-ing. According to the result of the experiment,this approach presented in this paper has high efficiency and more accurate rate compared with simple consistent combination multiple kernel learning method

相似文献/References:

[1]项响琴 汪彩梅.基于聚类高维空间算法的离群数据挖掘技术研究[J].计算机技术与发展,2010,(01):120.
 XIANG Xiang-qin,WANG Cai-mei.Study of Outlier Data Mining Based on CLIQUE Algorithm[J].,2010,(10):120.
[2]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
 LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(10):125.
[3]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
 LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].,2010,(10):143.
[4]吉同路 柏永飞 王立松.住宅与房地产电子政务中数据挖掘的应用研究[J].计算机技术与发展,2010,(01):235.
 JI Tong-lu,BAI Yong-fei,WANG Li-song.Study and Application of Data Mining in E-government of House and Real Estate Industry[J].,2010,(10):235.
[5]陈俏 曹根牛 陈柳.支持向量机应用于大气污染物浓度预测[J].计算机技术与发展,2010,(01):247.
 CHEN Qiao,CAO Gen-niu,CHEN Liu.Application of Support Vector Machine to Atmospheric Pollution Prediction[J].,2010,(10):247.
[6]李晶 姚明海.基于支持向量机的语义图像分类研究[J].计算机技术与发展,2010,(02):75.
 LI Jing,YAO Ming-hai.Research of Semantic Image Classification Based on Support Vector Machine[J].,2010,(10):75.
[7]杨静 张楠男 李建 刘延明 梁美红.决策树算法的研究与应用[J].计算机技术与发展,2010,(02):114.
 YANG Jing,ZHANG Nan-nan,LI Jian,et al.Research and Application of Decision Tree Algorithm[J].,2010,(10):114.
[8]赵裕啸 倪志伟 王园园 伍章俊.SQL Server 2005数据挖掘技术在证券客户忠诚度的应用[J].计算机技术与发展,2010,(02):229.
 ZHAO Yu-xiao,NI Zhi-wei,WANG Yuan-yuan,et al.Application of Data Mining Technology of SQL Server 2005 in Customer Loyalty Model in Securities Industry[J].,2010,(10):229.
[9]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
 JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(10):17.
[10]曹庆璞 董淑福 罗赟骞.网络时延的混沌特性分析及预测[J].计算机技术与发展,2010,(04):43.
 CAO Qing-pu,DONG Shu-fu,LUO Yun-qian.Chaotic Analysis and Prediction of Internet Time- Delay[J].,2010,(10):43.
[11]黄越 臧冽 聂盼盼.一种混合分类方法的研究与改进[J].计算机技术与发展,2012,(05):48.
 HUANG Yue,ZANG Lie,NIE Pan-pan.Research and Improvement of One Combination of Multiple Classifiers[J].,2012,(10):48.
[12]王萍萍,王翰虎.基于数据仓库技术的银行ACRM系统设计与实现[J].计算机技术与发展,2013,(03):203.
 WANG Ping-ping,WANG Han-hu.Design and Implementation of ACRM System for Bank Based on Data Warehouse Technology[J].,2013,(10):203.

更新日期/Last Update: 1900-01-01