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.
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Kasabov, N.: Evolving Connectionist Systems: The System Engineering Approach, 2nd edn. Springer, New York (2007)
Wysoski, S.G., Benuskova, L., Kasabov, N.: On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 61–70. Springer, Heidelberg (2006)
Hopfield, J.: Pattern Recognition Computation Using Action Potential Timing for Stimulus Representation. Nature 376, 33–36 (1995)
Bohte, S.M., Kok, J.N., La Poutre, H.: Error-Backpropagation in Temporally Encoded Networks of Spiking Neurons. Neurocomputing 48(1) (2002)
Thorpe, S.J.: How Can The Human Visual System Process A Natural Scene in Under 150ms? Experiments and Neural Network Models. In: ESANN (1997)
Schliebs, S., Defoin-Platel, M., Kasabov, N.: Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network. In: Köppen, M., et al. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 1229–1236. Springer, Heidelberg (2009)
Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network: Exploring Heterogeneous Probabilistic Models. Neural Networks 22, 623–632 (2009)
Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Press, NJ (1995)
Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transactions on Evolutionary Computation 6, 580–593 (2002)
Sun, J., Feng, B., Xu, W.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. Cong. Evolutionary Computation, CEC 2004, vol. 1, pp. 325–331 (2004)
Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. Automation and Remote Control 25, 821–837 (1964)
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text Classification Using String Kernels. Journal of Machine Learning Research 2, 419–444 (2002)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Kasabov, N.: Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 3–13. Springer, Heidelberg (2009)
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Abdull Hamed, H.N., Kasabov, N., Michlovský, Z., Shamsuddin, S.M. (2009). String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_68
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DOI: https://doi.org/10.1007/978-3-642-10684-2_68
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