A connectionist approach for incremental function approximation and on-line tasks

@inproceedings{Heinen2011ACA,
  title={A connectionist approach for incremental function approximation and on-line tasks},
  author={Milton Roberto Heinen},
  year={2011}
}
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 RESUMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 

Citations

Publications citing this paper.
SHOWING 1-10 OF 20 CITATIONS

Improving the Incremental Gaussian Mixture Neural Network model for spatial interpolation and geostatistical simulation

  • 2016 International Joint Conference on Neural Networks (IJCNN)
  • 2016
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Adaptive Incremental Gaussian Mixture Network for Non-Stationary Data Stream Classification

Jorge C. Chamby-Diaz, Mariana Recamonde Mendoza, Ana L. C. Bazzan, Ricardo Grunitzki
  • 2018 International Joint Conference on Neural Networks (IJCNN)
  • 2018
VIEW 6 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Optimal control with reinforcement learning using reservoir computing and Gaussian Mixture

  • 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings
  • 2012
VIEW 13 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Using a Gaussian mixture neural network for incremental learning and robotics

  • The 2012 International Joint Conference on Neural Networks (IJCNN)
  • 2012
VIEW 4 EXCERPTS
CITES METHODS

Scalable and Incremental Learning of Gaussian Mixture Models

VIEW 3 EXCERPTS
CITES RESULTS, BACKGROUND & METHODS
HIGHLY INFLUENCED

References

Publications referenced by this paper.