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SIU Thesis. A support vector machine approach to automatic email / Anmed, Mohamed Hussein / Bangkok : Shinawatra University - 2014

Public ISBD No review available on this record. Collection Title: SIU Thesis Title : A support vector machine approach to automatic email : multiclass classification Material Type: printed text Authors: Anmed, Mohamed Hussein, Author Publisher: Bangkok : Shinawatra University Publication Date: 2014 Pagination: vii, 43 p. Size: 30 cm. Price: Gift. General note: Thesis. [M.S. [Information Technology]]. - Shinawatra University, 2014. Languages : English (eng) Descriptors: [LCSH]Electronic mail messages [LCSH]Electronic mail systems [LCSH]Electronic mail systems -- management [LCSH]Information retrieval [LCSH]Information Storage and Retrieval [LCSH]Machine learning [LCSH]WWW (Information retrieval system) Keywords: Information retrieval. Electronic mail system. Machine learning. Class number: SIU THE: SOIT-MSIT-2014-01 Abstract: E-mail is a method of communication by sending and receiving digital messages among senders and recipients. Emails play a significant role in almost everyones’ daily activities. Typically users receive 20 to 30 or more messages per day and as the number of emails increase, users could unavoidably face an information overload which induces negative effects on the way users manage and organize their emails. Although, there are number of tools available for manual email management which focus on filtering messages by keyword, sender, etc., many of them are not very effective and troublesome to setup for novice users. This study focuses on email classification through grouping of messages based on their subjects and contents. The classification algorithms: the Supported Vector Machines (SVM) and Naïve Bayesian (NB) with kernel machines are studies and investigated. The study results reveal that SVM has a better performance than NB in both accuracy level and total computational time. Curricular : MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=24134 SIU Thesis. A support vector machine approach to automatic email : multiclass classification [printed text] / Anmed, Mohamed Hussein, Author . - Bangkok : Shinawatra University, 2014 . - vii, 43 p. ; 30 cm. Gift. Thesis. [M.S. [Information Technology]]. - Shinawatra University, 2014. Languages : English (eng) Descriptors: [LCSH]Electronic mail messages [LCSH]Electronic mail systems [LCSH]Electronic mail systems -- management [LCSH]Information retrieval [LCSH]Information Storage and Retrieval [LCSH]Machine learning [LCSH]WWW (Information retrieval system) Keywords: Information retrieval. Electronic mail system. Machine learning. Class number: SIU THE: SOIT-MSIT-2014-01 Abstract: E-mail is a method of communication by sending and receiving digital messages among senders and recipients. Emails play a significant role in almost everyones’ daily activities. Typically users receive 20 to 30 or more messages per day and as the number of emails increase, users could unavoidably face an information overload which induces negative effects on the way users manage and organize their emails. Although, there are number of tools available for manual email management which focus on filtering messages by keyword, sender, etc., many of them are not very effective and troublesome to setup for novice users. This study focuses on email classification through grouping of messages based on their subjects and contents. The classification algorithms: the Supported Vector Machines (SVM) and Naïve Bayesian (NB) with kernel machines are studies and investigated. The study results reveal that SVM has a better performance than NB in both accuracy level and total computational time. Curricular : MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=24134

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Barcode	Call number	Media type	Location	Section	Status
32002000399624	SIU THE: SOIT-MSIT-2014-01	SIU Thesis and Dissertation	Graduate Library	Thesis Corner	Available
32002000580496	SIU THE: SOIT-MSIT-2014-01 c.2	SIU Thesis and Dissertation	Graduate Library	Thesis Corner	Available