Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy

@article{Luo2018TowardsES,
  title={Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy},
  author={X. Luo and Y. Xu and Weiping Wang and Manman Yuan and X. Ban and Y. Zhu and Wenbing Zhao},
  journal={J. Frankl. Inst.},
  year={2018},
  volume={355},
  pages={1945-1966}
}
Abstract The stacked extreme learning machine (S-ELM) is an advanced framework of deep learning. It passes the ‘reduced’ outputs of the previous layer to the current layer, instead of directly propagating the previous outputs to the next layer in traditional deep learning. The S-ELM could address some large and complex data problems with a high accuracy and a relatively low requirement for memory. However, there is still room for improvement of the time complexity as well as robustness while… Expand
73 Citations
Broad Learning System Based on Maximum Correntropy Criterion
  • 3
  • PDF
Deep Residual Learning-based Reconstruction of Stacked Autoencoder Representation
  • H. Li, M. Trocan
  • Computer Science
  • 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
  • 2018
Robust Matching Pursuit Extreme Learning Machines
  • 2
  • PDF
Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
  • 94
Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction
  • 1
  • PDF
A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data
  • 2
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 35 REFERENCES
Efficient and robust deep learning with Correntropy-induced loss function
  • 49
  • PDF
Stacked Extreme Learning Machines
  • 97
Extreme Learning Machine for Multilayer Perceptron
  • 780
  • PDF
Training extreme learning machine via regularized correntropy criterion
  • 32
Incremental extreme learning machine based on deep feature embedded
  • 36
...
1
2
3
4
...