Efficient parallel learning algorithms for neural networks
Advances in neural information processing systems 1
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Supervised Latent Semantic Indexing for Document Categorization
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Improving neural networks generalization with new constructive and pruning methods
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
Parallelizing neural network training for cluster systems
PDCN '08 Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks
Integer factorization by a parallel GNFS algorithm for public key cryptosystems
ICESS'05 Proceedings of the Second international conference on Embedded Software and Systems
Parallel genetic algorithms for DVS scheduling of distributed embedded systems
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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This paper studies parallel training of an improved neural network for text categorization. With the explosive growth on the amount of digital information available on the Internet, text categorization problem has become more and more important, especially when millions of mobile devices are now connecting to the Internet. Improved back-propagation neural network (IBPNN) is an efficient approach for classification problems which overcomes the limitations of traditional BPNN. In this paper, we utilize parallel computing to speedup the neural network training process of IBPNN. The parallel IBNPP algorithm for text categorization is implemented on a Sun Cluster with 34 nodes (processors). The communication time and speedup for the parallel IBPNN versus various number of nodes are studied. Experiments are conducted on various data sets and the results show that the parallel IBPNN together with SVD technique achieves fast computational speed and high text categorization correctness.