Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering

@article{Raitoharju2016TrainingRB,
  title={Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering},
  author={Jenni Raitoharju and Serkan Kiranyaz and Moncef Gabbouj},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2016},
  volume={27},
  pages={2458-2471}
}
In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 54 REFERENCES

Fractional Particle Swarm Optimization in Multidimensional Search Space

  • IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2010
VIEW 5 EXCERPTS

Bayesian Ying-Yang machine, clustering and number of clusters

  • Pattern Recognition Letters
  • 1997
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Chondrodima, “A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing,

E. A. Alexandridis
  • J. Biomed. Inform.,
  • 2014
VIEW 1 EXCERPT