A neuro-SVM model for text classification using latent semantic indexing

This paper presents a new model integrating a recurrent neural network (RNN) and a least squares support vector machine (LS-SVM) for classification of document titles according to different predetermined categories. The new model proposed in this paper is abbreviated as neuro-SVM. Based on the neuro...

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Bibliographic Details
Main Authors: Vikramjit Mitra, Chia-Jiu Wang, Satarupa Banerjee
Format: Conference Proceeding
Language:English
Subjects:
Indexing
Information retrieval
Large scale integration
Libraries
Material storage
Recurrent neural networks
Sparse matrices
Support vector machine classification
Support vector machines
Text categorization
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Description
Summary:This paper presents a new model integrating a recurrent neural network (RNN) and a least squares support vector machine (LS-SVM) for classification of document titles according to different predetermined categories. The new model proposed in this paper is abbreviated as neuro-SVM. Based on the neuro-SVM model, a system is implemented, using latent semantic indexing (LSI) to generate probabilistic coefficients from document titles, which are used as the input to the system. The system's performance is demonstrated with a corpus of 96956 words, from University of Denver's Penrose library catalogue and the accuracy rate of the proposed system is found to be 99.66%.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2005.1555893