C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
QARAB: a question answering system to support the Arabic language
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Text categorization is one of the well studied problems in data mining and information retrieval. Given a large quantity of documents in a data set where each document is associated with its corresponding category. Categorization involves building a model from classified documents, in order to classify previously unseen documents as accurately as possible. This paper investigates Naïve Bayesian method (NB) and Support Vector Machine (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorisation data sets reveal that SVM algorithm outperforms the NB with regards to all measures.