String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization

  • Haza Nuzly Abdull Hamed
  • Nikola Kasabov
  • Zbynek Michlovský
  • Siti Mariyam Shamsuddin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5864)

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.

Keywords

String Kernels Text Classification Evolving Spiking Neural Network Particle Swarm Quantum Computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Haza Nuzly Abdull Hamed
    • 1
  • Nikola Kasabov
    • 1
  • Zbynek Michlovský
    • 2
  • Siti Mariyam Shamsuddin
    • 3
  1. 1.Knowledge Engineering and Discovery Research Institute (KEDRI)Auckland University of TechnologyNew Zealand
  2. 2.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic
  3. 3.Soft Computing Research GroupUniversiti Teknologi MalaysiaMalaysia

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