From this page you can:
| Home |
Search results
4 result(s) search for keyword(s) 'Machine learning' 
Add the result to your basket Refine your search Apply to external sources Make a suggestionUSING PUBLICATIONS AND DOMAN KNOWLEDGE TO BUILD RESERACH PROFIT / Biswas, Md. Humayun Kabir
Title : USING PUBLICATIONS AND DOMAN KNOWLEDGE TO BUILD RESERACH PROFIT Original title : AN APPLICATION IN AUTOMATIC REVIEWER ASSIGNMENT Material Type: printed text Authors: Biswas, Md. Humayun Kabir, Author Pagination: 78 Pages Layout: ill. Size: 30 cm. Price: 1500.00 Languages : English (eng) Descriptors: [LCSH]Machine learning
[LCSH]Machine learning -- TechniqueKeywords: Machine learning Curricular : MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=22769 USING PUBLICATIONS AND DOMAN KNOWLEDGE TO BUILD RESERACH PROFIT = AN APPLICATION IN AUTOMATIC REVIEWER ASSIGNMENT [printed text] / Biswas, Md. Humayun Kabir, Author . - [s.d.] . - 78 Pages : ill. ; 30 cm.
1500.00
Languages : English (eng)
Descriptors: [LCSH]Machine learning
[LCSH]Machine learning -- TechniqueKeywords: Machine learning Curricular : MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=22769 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 32002000336733 SIU THE SOT-MSIT 2006-01 Thesis Graduate Library Thesis Corner Available
Collection Title: SIU SS Title : Sentiment Analysis Material Type: printed text Authors: Jyotsna Das, Author Publisher: Bangkok: Shinawatra University Publication Date: 2010 Pagination: vi, 39 p. Layout: ill, tables Size: 30 cm. Price: 500.00 General note: SIU SS: SOT-PhD-2010-01
Special Study. [Ph.D [Philosophy in Technology]] -- Shinawatra University, 2010Languages : English (eng) Descriptors: [LCSH]Natural language processing (Computer science) Keywords: Artificial Intelligence,
Machine Learning,
Natural Language Processing,
Text MiningAbstract: Sentiment Analysis which is also used synonymously with Opinion Mining is a method to analyze the opinion expressed in documents, social networking sites, blogs, twitter, web forums and all other sites where users express their opinion. With the advancement of the Internet and its applications, it has become the prime domain for the expression of views by people.
The Special Study aims to study how different researchers have tried to find the sentiment or opinion in different kinds of reviews. It also aims to analyze the different approaches that have been undertaken to arrive at it.
The scope of this study is to do a literature review of key work done in this area, the steps involved in carrying out this kind of research, the various methods adopted by different researchers, to discuss the framework of one of the researcher’s work for a complete overview and to compare those reviews.
It touches upon the areas of Information Retrieval and Computational Linguistics
Curricular : BSCS/MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=26234 SIU SS. Sentiment Analysis [printed text] / Jyotsna Das, Author . - [S.l.] : Bangkok: Shinawatra University, 2010 . - vi, 39 p. : ill, tables ; 30 cm.
500.00
SIU SS: SOT-PhD-2010-01
Special Study. [Ph.D [Philosophy in Technology]] -- Shinawatra University, 2010
Languages : English (eng)
Descriptors: [LCSH]Natural language processing (Computer science) Keywords: Artificial Intelligence,
Machine Learning,
Natural Language Processing,
Text MiningAbstract: Sentiment Analysis which is also used synonymously with Opinion Mining is a method to analyze the opinion expressed in documents, social networking sites, blogs, twitter, web forums and all other sites where users express their opinion. With the advancement of the Internet and its applications, it has become the prime domain for the expression of views by people.
The Special Study aims to study how different researchers have tried to find the sentiment or opinion in different kinds of reviews. It also aims to analyze the different approaches that have been undertaken to arrive at it.
The scope of this study is to do a literature review of key work done in this area, the steps involved in carrying out this kind of research, the various methods adopted by different researchers, to discuss the framework of one of the researcher’s work for a complete overview and to compare those reviews.
It touches upon the areas of Information Retrieval and Computational Linguistics
Curricular : BSCS/MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=26234 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 32002000591055 SIU SS: SOT-PhD-2010-01 c.1 SIU Special Study Graduate Library Thesis Corner Available
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 Hold
Place a hold on this item
Copies
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
Title : การเรียนรู้ของเครื่อง Machine Learning Material Type: printed text Authors: นวลวรรณ สุนทรภิษัช, Author Publisher: กรุงเทพฯ: ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยเกษตรศาสตร์ Publication Date: 2563 Pagination: xii, 230 น. Layout: ตาราง, ภาพประกอบ Size: 26 ซม. ISBN (or other code): 978-6-16-572203-2 Price: 200.00 บาท General note: บทที่ 1 บทนำการเรียนรู้ของเครื่อง -- บทที่ 2 ต้นไม้ตัดสินใจ -- บทที่ 3 เบย์อย่างง่าย -- บทที่ 4 เพื่อนบ้านใกล้สุด k ตัว -- บทที่ 5 ขั้นตอนวิธีการถดถอย -- บทที่ 6 โครงข่ายประสาทเทียม -- บทที่ 7 การเรียนรู้เชิงลึก -- บทที่ 8 ซัพพอร์ตเวกเตอร์แมชชีน -- บทที่ 9 ขั้นตอนวิธีการจัดกลุ่ม -- บทที่ 10 อองเซมเบิล -- บทที่ 11 ปัญหาคลาสไม่สมดุล -- บทที่ 12 วิธีคัดเลือกฟีเจอร์ -- บทที่ 13 กรณีศึกษา Languages : Thai (tha) Descriptors: [LCSH]การเรียนรู้
[LCSH]ทฤษฎีการเรียนรู้
[LCSH]เทคโนโลยีKeywords: การเรียนรู้, Machine learning, เทคโนโลยี Class number: Q325.5 น341ก 2563 Curricular : BSCS/GE/MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=28284 การเรียนรู้ของเครื่อง Machine Learning [printed text] / นวลวรรณ สุนทรภิษัช, Author . - [S.l.] : กรุงเทพฯ: ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยเกษตรศาสตร์, 2563 . - xii, 230 น. : ตาราง, ภาพประกอบ ; 26 ซม.
ISBN : 978-6-16-572203-2 : 200.00 บาท
บทที่ 1 บทนำการเรียนรู้ของเครื่อง -- บทที่ 2 ต้นไม้ตัดสินใจ -- บทที่ 3 เบย์อย่างง่าย -- บทที่ 4 เพื่อนบ้านใกล้สุด k ตัว -- บทที่ 5 ขั้นตอนวิธีการถดถอย -- บทที่ 6 โครงข่ายประสาทเทียม -- บทที่ 7 การเรียนรู้เชิงลึก -- บทที่ 8 ซัพพอร์ตเวกเตอร์แมชชีน -- บทที่ 9 ขั้นตอนวิธีการจัดกลุ่ม -- บทที่ 10 อองเซมเบิล -- บทที่ 11 ปัญหาคลาสไม่สมดุล -- บทที่ 12 วิธีคัดเลือกฟีเจอร์ -- บทที่ 13 กรณีศึกษา
Languages : Thai (tha)
Descriptors: [LCSH]การเรียนรู้
[LCSH]ทฤษฎีการเรียนรู้
[LCSH]เทคโนโลยีKeywords: การเรียนรู้, Machine learning, เทคโนโลยี Class number: Q325.5 น341ก 2563 Curricular : BSCS/GE/MSIT Record link: http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=28284 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 32002000607935 Q325.5 น341ก 2563 Book Main Library General Shelf Available
