The Softmap Algorithm

@article{Raekelboom1998TheSA,
  title={The Softmap Algorithm},
  author={Steven Raekelboom and Marc M. Van Hulle},
  journal={Neural Processing Letters},
  year={1998},
  volume={8},
  pages={181-192}
}
A new unsupervised competitive learning rule is introduced, called the Self-organizing free-topology map (Softmap) algorithm, for nonparametric density estimation. The receptive fields of the formal neurons are overlapping, radially-symmetric kernels, the radii of which are adapted to the local input density together with the weight vectors which define the kernel centers. A fuzzy code membership function is introduced in order to encompass, in a novel way, the presence of overlapping receptive… CONTINUE READING

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
0 Extracted Citations
12 Extracted References
Similar Papers

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 12 references

Nonparametric Density Estimation and -regression Achieved with a Learning Rule for Equiprobabilistic Topographic Map Formation

  • M. M. Van Hulle
  • in Proc. IEEE NNSP96,
  • 1996
Highly Influential
9 Excerpts

Density Estimation for Statistics and Data

  • B. W. Silverman
  • 1992
Highly Influential
5 Excerpts

Self-organizing maps

  • T. Kohonen
  • 1995
2 Excerpts

On bandwidth variation of kernel estimates – a square root law

  • I. S. Abramson
  • Ann. Statist.,
  • 1982
1 Excerpt

Variable kernel estimates of multivariate densities

  • L. Breiman, W. Meisel, E. Purcell
  • Technometrics, Vol
  • 1977
2 Excerpts

Similar Papers

Loading similar papers…