Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Cyberspace 2000: dealing with information overload
Communications of the ACM
Information retrieval on the web
ACM Computing Surveys (CSUR)
Profiling students' adaptation styles in Web-based learning
Computers & Education
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Personalized e-learning system using Item Response Theory
Computers & Education
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Personalized web-based tutoring system based on fuzzy item response theory
Expert Systems with Applications: An International Journal
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Subtree mining for question classification problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Question classification by structure induction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Expert Systems with Applications: An International Journal
Fuzzy preference based rough sets
Information Sciences: an International Journal
Kerneltron: support vector "machine" in silicon
IEEE Transactions on Neural Networks
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In this study, a ranking test problem of Computer Adaptive Testing (CAT) is benchmarked by employing three popular classifiers: Artificial Neural Network (ANN), Support Vector Machines (SVMs), and Adaptive Network Based Fuzzy Inference System (ANFIS) in terms of ordinal classification performances. As the pilot test, "History of Civilization" class which offered in Bahcesehir University is selected. Item Response Theory (IRT) is focused for the determination of system inputs which are item responses of students, item difficulties of questions, and question levels. Item difficulties of questions are Gaussian normalized to make ordinal decisions. The distance between predicted and expected class values is employed for accuracy estimation. Comparison study is conducted to the ordinal class prediction correctness and performance analysis which is observed by Receiver Operating Characteristic (ROC) graphs. The results show that ANFIS has better performance and higher accuracy than ANN and SVMs in terms of ordinal question classification when the ordinal decisions are practically made by Gaussian Normal Distribution and ROC graphs are focused to observe any significant difference among the performances of classifiers.