Ensemble of structure-adaptive self-organizing maps for high performance classification

@article{Cho2000EnsembleOS,
  title={Ensemble of structure-adaptive self-organizing maps for high performance classification},
  author={Sung-Bae Cho},
  journal={Inf. Sci.},
  year={2000},
  volume={123},
  pages={103-114}
}
Combining multiple models has been recently exploited for the development of reliable neural networks. This paper introduces a structure-adaptive self-organizing map (SOM) which can adapt the structure as well as the weights, and presents a method to improve the performance by combining the multiple maps. The structure-adaptive SOM places the nodes of prototype vectors into the pattern space properly so as to make the decision boundaries as close to the class boundaries as possible. In order to… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 19 references

Recognition of handwritten numerals based on the concept of multiple experts

  • C. Y. Suen, C. Nadal, T. Mai, R. Legault, L. Lam
  • in: Proceedings of the First International…
  • 2000
Highly Influential
4 Excerpts

O€-line recognition of unconstrained handwritten digits using multilayer backpropagation neural network combined with genetic algorithm

  • Y. J. Kim, S. W. Lee
  • in: Proceedings of the Sixth Workshop on Image…
  • 1994
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…