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String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization

Adbull Hamed, Haza Nuzly and Kasabov, Nikola and Michlovsky, Zbynek and Shamsuddin, Siti Mariyam (2009) String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization. In: Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II. Lecture Notes in Computer Science . Springer, Berlin/ Heidelberg, pp. 611-619. ISBN 978-3-642-10682-8

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Official URL: http://dx.doi.org/10.1007/978-3-642-10684-2_68

Abstract

This paper proposes a novel method for string pattern recognition using an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism optimizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features.

Item Type:Book Section
Uncontrolled Keywords:string kernels, text classification, evolving spiking neural network, particle swarm, quantum computing
Subjects:T Technology > T Technology (General)
Divisions:Computer Science and Information System (Formerly known)
ID Code:14782
Deposited By: Siti Khairiyah Nordin
Deposited On:11 Sep 2011 09:18
Last Modified:29 Apr 2013 04:54

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