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Application for Web Text Categorization Based on Support Vector Machine

Author: DuanYing
Tutor: PanZuo
School: Wuhan University of Technology
Course: Applied Computer Technology
Keywords: Web Data Mining Text classification techniques Bayesian minimum error rate Support Vector Machine (SVM)
CLC: TP391.1
Type: Master's thesis
Year: 2010
Downloads: 91
Quote: 1
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Abstract


With the Web as one of the most important applications of the Internet , it provides a convenient document publishing and information access , and gathered around the information resources on the Internet has become an indispensable part of life . Display according to official information , the Internet has billions of dollars of Web documents , Web users often impossible to start the face of such a large mass of information , quick access to the information they need , there is an urgent need a way to quickly locate useful information on . Web data mining technology have given rise to more and more because of this demand . At this stage of the Web data mining is primarily based on information retrieval , data mining , and knowledge management , knowledge implied by the analysis of a large number of Web documents and patterns , which makes it better for information search . With the development of Web data mining technology , today's text classification techniques can improve the text information clutter situation can reduce query time , to improve search quality , fast and efficient access to text information . Automatic text classification technology , more and more people pay attention . Machine learning - based text automatic classification has achieved good results, a variety of classification algorithms , such as k-nearest neighbor algorithm , Naive Bayes algorithm , decision tree algorithm and support vector machine . This article focuses on Web data mining the Chinese classification techniques , given the process of Web text classification : text preprocessing, feature reduction, text features representations of support vector machine (SVM) classification algorithm in text classification application . Focus on the combination with the minimum error rate in the Bayesian support vector machine to construct a multi- classification Web text classification model as well as its specific tectonic processes . The experiments show that , under the conditions to ensure that the performance of the classifier , select the training data samples for training , the experimental results than the traditional support vector machine classifier accuracy improved , with higher operating efficiency .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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