An improved term weighting scheme for text categorization
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: An improved term weighting scheme for text categorization
An improved term weighting scheme for text categorization
An Improved Term Weighting Scheme for Text CategorizationPham Xuan NguyenFaculty of Information Technology University of Engineering and Technology Vi An improved term weighting scheme for text categorization ietnam National University, HanoiSupervised by Dr. Le Quang Hit'llA thesis submitted in fulfillment of the requirements for the degree ofMaster of Science in Computer Science41852ORIGINALITY STATEMENT‘1 hereby declare that this siibniisúoii is my own work, 'lb t he best of my knowledge, it contains An improved term weighting scheme for text categorization no materials previously published by another person, or substantial proportions of material which have bextn accepted for the award of any other degreAn improved term weighting scheme for text categorization
es or diplomas at. University of Engineering anil TAn improved term weighting scheme for text categorization
is lhe task to assign weights to terms (luring the document presentation phase. Thus, it affects the classification performance. In addition to resulAn Improved Term Weighting Scheme for Text CategorizationPham Xuan NguyenFaculty of Information Technology University of Engineering and Technology Vi An improved term weighting scheme for text categorization two categories, namely, supervised and unsupervised [27]. The traditional term weighting schemes such as binary, if and ff.idf |38], belong to unsupervised term weighting methods. Other schemes (for example, tf.\2 [12]) that make use of the prior information about the membership of training documen An improved term weighting scheme for text categorization ts, belong to the supervised term weighting methods.The supervised term weighting method tf.rf [27] is one of the most effective schemes to date. It sAn improved term weighting scheme for text categorization
howed better performance than many others (27). However, tf.rf is not the best in some cases. Moreover, tf.rf requires many rf values for each term.InAn Improved Term Weighting Scheme for Text CategorizationPham Xuan NguyenFaculty of Information Technology University of Engineering and Technology Vi An improved term weighting scheme for text categorization Furthermore, our scheme is simpler than tf.rf because it only uses the maximum value of if for each term. Our experimental results showed that our scheme is consistently better than {/.if and ot hers.iiTo my family Ọ:::ACKNOWLEDGEMENTSFirst, I would like to express my gratitude to my supervisor, Dr An improved term weighting scheme for text categorization . I.e Quang Hieu. He guided Hie throughout the years and gave Hie several useful advices about study method. He was very patient with me. His words inAn improved term weighting scheme for text categorization
fluenced strongly on me. I also would like to give my honest appreciation to my colleagues at Hoahi University and University of Engineering and IfechAn Improved Term Weighting Scheme for Text CategorizationPham Xuan NguyenFaculty of Information Technology University of Engineering and Technology Vi An improved term weighting scheme for text categorization ........ 11.2Structure of this Thesis ..................................... 22Overview of Text Categorization42.1Introduction.................................................. 4 An improved term weighting scheme for text categorization An Improved Term Weighting Scheme for Text CategorizationPham Xuan NguyenFaculty of Information Technology University of Engineering and Technology ViGọi ngay
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