Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

This paper presents a novel method for string classification using ESNN-QiPSO. The experiments have shown that ESNN with parameter optimization and using a small number of features produces promising results that is significant for future exploration.  Other work includes how to find a more effective method for choosing the most relevant features and eliminating irrelevant features.

Anglický abstrakt

This paper presents a novel method for string classification using ESNN-QiPSO. The experiments have shown that ESNN with parameter optimization and using a small number of features produces promising results that is significant for future exploration.  Other work includes how to find a more effective method for choosing the most relevant features and eliminating irrelevant features.

BibTex 


@inproceedings{BUT34296,
  author="Zbyněk {Michlovský}",
  title="String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization",
  annote="This paper presents a novel method for string classification using ESNN-QiPSO.
The experiments have shown that ESNN with parameter optimization and using
a small number of features produces promising results that is significant for
future exploration.  Other work includes how to find a more effective method for
choosing the most relevant features and eliminating irrelevant features.",
  address="Springer Verlag",
  booktitle="Neural Information Processing",
  chapter="34296",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2009",
  month="december",
  pages="611--619",
  publisher="Springer Verlag",
  type="conference paper"
}