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  1. Myanmar Institute of Information Technology
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Comparison of Naive Bayes and Support Vector Machine Classifiers on Document Classification

http://hdl.handle.net/20.500.12678/0000006797
http://hdl.handle.net/20.500.12678/0000006797
e4cd88ca-4e75-48a6-98e7-9320ed5a976b
4a11c001-058c-4a21-b73b-09d8df20a01f
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Comparison Comparison of Naive Bayes and Support Vector Machine Classifier (1).pdf (915 KB)
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Publication type
Conference paper
Upload type
Publication
Title
Title Comparison of Naive Bayes and Support Vector Machine Classifiers on Document Classification
Language en
Publication date 2018-10-09
Authors
Hlaing May Tin
Zun Hlaing Moe
Thida San
Mie Mie Khin
Description
The main idea of this paper is to classify the field from IT research papers and compare the accuracy in two classifiers. The most important features are selected and data are prepared for learning and classification. Training and classification are performed using Naive Bayes and Support Vector Machine (SVM) classifiers.
Keywords
Naive Bayes, Classification, Text, SVM, Categories, Accuracy
Identifier ISBN: 978-1-5356-6310-3
Conference papers
9/10/2018
2018 IEEE 7th Global Conference on Consumer Electronics
Nara Royal Hotel, Nara, Japan hosted by IEEE Society
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