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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/20.500.12678/0000006797e4cd88ca-4e75-48a6-98e7-9320ed5a976b
4a11c001-058c-4a21-b73b-09d8df20a01f
Name / File | License | Actions |
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Publication type | ||||||
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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 |