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  • 标题:Machine Learning Algorithms in Web Page Classification
  • 本地全文:下载
  • 作者:W. A. AWAD
  • 期刊名称:International Journal of Computer Science & Information Technology (IJCSIT)
  • 印刷版ISSN:0975-4660
  • 电子版ISSN:0975-3826
  • 出版年度:2012
  • 卷号:4
  • 期号:5
  • 页码:93
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:In this paper we use machine learning algorithms like SVM, KNN and GIS to perform a behaviorcomparison on the web pages classifications problem, from the experiment we see in the SVM with smallnumber of negative documents to build the centroids has the smallest storage requirement and the least online test computation cost. But almost all GIS with different number of nearest neighbors have an evenhigher storage requirement and on line test computation cost than KNN. This suggests that some futurework should be done to try to reduce the storage requirement and on list test cost of GIS.
  • 关键词:Web Classifications; Machine Learning; LIBSVM; SVM; K-NN.
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