Dealing with learning concepts via support vector machines
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Date
2014
Authors
Korhan Günel
Rifat Aşliyan
Mehmet Kurt
Refet Polat
Turgut Ozis
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Verlag service@springer.de
Open Access Color
Green Open Access
Yes
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No
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Abstract
Extracting learning concepts is one of the major problems of artificial intelligence on education. Essentially the determination of learning concepts within an educational content has some differences as compared with keyword or technical term extraction process. However the problem can still taught as a classification problem notwithstanding. In this paper we examine how to handle the extraction of learning concepts using support vector machines as a supervised learning algorithm and we evaluate the performance of the proposed approach using f-measure. © Springer-Verlag Berlin Heidelberg 2014. © 2016 Elsevier B.V. All rights reserved.
Description
Keywords
Classification, Intelligent Tutoring Systems, Machine Learning, Support Vector Machines, Text Mining, Artificial Intelligence, Classification (of Information), Computer Aided Instruction, Data Mining, Information Technology, Learning Algorithms, Learning Systems, Management Science, Text Processing, Educational Contents, F-measure, Intelligent Tutoring System, Technical Terms, Text Mining, Support Vector Machines, Artificial intelligence, Classification (of information), Computer aided instruction, Data mining, Information technology, Learning algorithms, Learning systems, Management science, Text processing, Educational contents, F-measure, Intelligent tutoring system, Technical terms, Text mining, Support vector machines, Text Mining, Support Vector Machines, Classification, Machine Learning, Intelligent Tutoring Systems, Text mining, Support Vector Machines, Machine learning, Classification, Intelligent tutoring systems
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Citation
WoS Q
Scopus Q
OpenCitations Citation Count
2
Source
7th International Conference on Management Science and Engineering Management ICMSEM 2013
Volume
241 LNEE
Issue
VOL. 1
Start Page
61
End Page
71
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CrossRef : 2
Scopus : 3
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