Abstract
This paper described two kinds of neural networks for text categorization, multi-output perceptron learning (MOPL) and back-propagation neural network (BPNN), and then we proposed a novel algorithm using improved back-propagation neural network. This algorithm can overcome some shortcomings in traditional back-propagation neural network such as slow training speed and easy to enter into local minimum. We compared the training time and the performance, and tested the three methods on the standard Reuter-21578. The results show that the proposed algorithm is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure.
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Li, C.H., Park, S.C. (2006). Text Categorization Based on Artificial Neural Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_35
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DOI: https://doi.org/10.1007/11893295_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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