@article {287,
	title = {Evaluation of topic identification methods on arabic corpora},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):4Export Date: 10 July 2014},
	month = {2011},
	pages = {185 - 192},
	abstract = {Topic Identification is one of the important keys for the success of many applications. Indeed, there are few works in this field concerning Arabic language because of lack of standard corpora. In this study, we will provide directly comparable results of six text categorization methods on a new Arabic corpus Alwatan-2004. Hence, Topic Unigram Language Model (TULM), Term Frequency/Inverse Document Frequency (TFIDF), Neural Network, SVM, M-SVM and TR have been experimented, and showed that TR-Classifier is the most efficient among the set of classifiers, nevertheless, only binary SVM outperformed it thanks to its characteristics. Moreover, we should note that the size of Alwatan-2004 corpus used to achieve our experiments is considered the most important compared to any other Arabic corpus which had been used for topic identification experiments until now. In addition, we aim through using small sizes of vocabularies to reduce the time of computation. This is important for adaptive language modeling, particularly Topic Adaptation, which is required in real time applications such as speech recognition and machine translation systems. Our experiments indicate that the results are better than other works dealing with Arabic text categorization.},
	keywords = {Alwatan-2004 corpus, Arabic language, Neural network, SVM, Topic Identification, TR, TULM},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855410016\&partnerID=40\&md5=8736f4304c67d00c33512bbcb14394fa},
	author = {Abbas, M. and Smaili, K. and Berkani, D.}
}
@article {239,
	title = {Improving Arabic text categorization using Neural Network with SVD},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2010},
	pages = {233 - 239},
	abstract = {In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. To test the effectiveness of the proposed model, experiments were conducted using an in-house collected Arabic corpus for text categorization. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by precision, recall and F-measure. Experimental result shows that the ANN model using SVD is better than the basic ANN on Arabic text classification.},
	keywords = {Arabic language, MLP, Neural network, Singular Value Decomposition, Text categorization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685734\&partnerID=40\&md5=231f569e3eb222816e6af0bb1e4a5cc4},
	author = {Harrag, F. and Al-Qawasmah, E.}
}
@article {87,
	title = {Multi-source remote sensing classification based on Mallat fusion and residual error feature selection},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {130 - 137},
	abstract = {Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.},
	keywords = {Feature selection, Mallet fusion, Multi-source classification, Neural network, Residual error},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350668378\&partnerID=40\&md5=8e01a98aa0645ce6934545475591db77},
	author = {Cao, D. and Yin, Q. and Guo, P.}
}
