Email categorization using support vector machine

Study on text categorization field contains classification process of text documents into a fixed number of pre-defined categories by user. The objective of this project is to make research on classifying email process based on category using Support Vector Machine software. Among processes will be...

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Bibliographic Details
Main Author: Mohd. Daud, Mariah
Format: Thesis
Language:English
Published: 2004
Subjects:
Online Access:http://eprints.utm.my/3297/1/MariahMohdDaudMFC2004.pdf
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author Mohd. Daud, Mariah
author_facet Mohd. Daud, Mariah
author_sort Mohd. Daud, Mariah
collection ePrints
description Study on text categorization field contains classification process of text documents into a fixed number of pre-defined categories by user. The objective of this project is to make research on classifying email process based on category using Support Vector Machine software. Among processes will be used are read input data email from subject and body, feature extraction, feature selection and classify data using Support Vector Machine (SVM). Feature extraction process involved word stopping and word stemming methods that can reduce the number of dimension of features. Features selection process involved TFIDF method. Effective of classification process has been measured using precision and recall criteria. Result produced from analysis showed that Support Vector Machine is very effective in email classifying process.
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spelling utm.eprints-32972018-06-26T07:56:26Z http://eprints.utm.my/3297/ Email categorization using support vector machine Mohd. Daud, Mariah QA75 Electronic computers. Computer science QA76 Computer software Study on text categorization field contains classification process of text documents into a fixed number of pre-defined categories by user. The objective of this project is to make research on classifying email process based on category using Support Vector Machine software. Among processes will be used are read input data email from subject and body, feature extraction, feature selection and classify data using Support Vector Machine (SVM). Feature extraction process involved word stopping and word stemming methods that can reduce the number of dimension of features. Features selection process involved TFIDF method. Effective of classification process has been measured using precision and recall criteria. Result produced from analysis showed that Support Vector Machine is very effective in email classifying process. 2004 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/3297/1/MariahMohdDaudMFC2004.pdf Mohd. Daud, Mariah (2004) Email categorization using support vector machine. Other thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Mohd. Daud, Mariah
Email categorization using support vector machine
title Email categorization using support vector machine
title_full Email categorization using support vector machine
title_fullStr Email categorization using support vector machine
title_full_unstemmed Email categorization using support vector machine
title_short Email categorization using support vector machine
title_sort email categorization using support vector machine
topic QA75 Electronic computers. Computer science
QA76 Computer software
url http://eprints.utm.my/3297/1/MariahMohdDaudMFC2004.pdf
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