Performance of MLP and RBF neural networks on arabic text categorization using SVD

Title:

Performance of MLP and RBF neural networks on arabic text categorization using SVD

Creator:

Harrag, Fouzi, Hamdi-Cherif, Aboubekeur, and El-Qawasmeh , Eyas

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:450d9217-0a9a-4d4e-9f17-665b14b1b8db
uuid:450d9217-0a9a-4d4e-9f17-665b14b1b8db

Subject:

Singularnvalue decomposition, neural network, MLP, RBF, arabic categorization, and natural language processing

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Text categorization is based on the idea of content-based texts clustering. An Artificial Neural Network (ANN) or simply Neural Network (NN) classifier for Arabic texts categorization is proposed. The Singular Value Decomposition (SVD) is used as preprocessor with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) classifiers are implemented. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed SVD-Supported MLP/RBF ANN classifier is able to achieve high effectiveness. Experimental results also show that the MLP classifier outperforms the RBF classifier and that the SVD-supported NN classifier is better than the basic NN, as far as Arabic text categorization is concerned.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:4

Harvested from:

CDK

Metadata only:

false