基于LIBSVM的视觉信息页面块分析模型
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摘要
随着网络技术的发展,逐渐的每个人都需要面对网络,浏览不同的网页,而网页页面的组成结构复杂,人的感官对于页面的认识与人眼所见并不相同。本文通过对于5473条页面的数据先分析再处理,然后把每一页的属性设置为10个,最后在基于支持向量机的基础上建立分类模型,对复杂的属性数据,我们应用LIBSVM先对其进行分析然后再根据分析结果对其进行处理、紧接着对其优化和解释,分类结果最终的准确率是97%,从而给网页的更深层的认识提供了有效的理论依据。
With the development of network technology,everyone needs to face the different websites. Web page structure is complex,and the understanding of human senses on page is different from what human eye see. In this paper,through the analysis of 5473 pieces of page data,the attribute of each page is set as 10,and visual information page classification model based on support vector machine( SVM) model is established. LIBSVM is used for analysis,processing,optimization and explanation of complex attribute data,and the accuracy rate of the classification results is as high as 97%,which provides a effective theoretical basis for a deeper understanding of webpage.
With the development of network technology,everyone needs to face the different websites. Web page structure is complex,and the understanding of human senses on page is different from what human eye see. In this paper,through the analysis of 5473 pieces of page data,the attribute of each page is set as 10,and visual information page classification model based on support vector machine( SVM) model is established. LIBSVM is used for analysis,processing,optimization and explanation of complex attribute data,and the accuracy rate of the classification results is as high as 97%,which provides a effective theoretical basis for a deeper understanding of webpage.
引文
[1]任立辉,李文东,慈兴华,等.基于LIBSVM的石油录井中岩屑岩性识别方法研究[J].中国海洋大学学报(自然科学版),2010,2(1):93-96.
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[3]龙明海,李长昱,向虎,等.支持向量机在卷烟配方原料分组中的应用[J].云南农业大学学报(自然科学),2017,11(1):93-95.
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[6]张益铭,徐晓钟,王智庆.支持向量机与时间序列预测综述[J].计算机应用与软件,2010,12(1):14-15.
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[8]高媛媛,刘强国.基于LIBSVM的葡萄酒品质评判模型[J].四川理工学院学报(自然科学版),2010,10(1):20-21.
[2]董健林.基于LIBSVM的石油录井中岩屑岩性识别方法研究[J].互联网文档资源,2003,3(2):62-64.
[3]龙明海,李长昱,向虎,等.支持向量机在卷烟配方原料分组中的应用[J].云南农业大学学报(自然科学),2017,11(1):93-95.
[4]芮璋现,肖海波.支持向量机(SVM)及其应用[J].福建电脑,2007,8(1):193-196.
[5]王慧勤,雷刚.基于LIBSVM的风速预测方法研究[J].科学技术与工程.2011,8(1):7-8.
[6]张益铭,徐晓钟,王智庆.支持向量机与时间序列预测综述[J].计算机应用与软件,2010,12(1):14-15.
[7]邓乃扬,田英杰.数据挖掘中的新方法---支持向量机[M].北京:科学技术出版社,2004:62-64.
[8]高媛媛,刘强国.基于LIBSVM的葡萄酒品质评判模型[J].四川理工学院学报(自然科学版),2010,10(1):20-21.