You are now following this Submission
- You will see updates in your followed content feed
- You may receive emails, depending on your communication preferences
Training for fast randomized single hidden layer feed-forward networks. It contains four configurations:
no direct link , with bias:
[Randomized SLFN] Wouter F Schmidt, Martin Kraaijveld, Robert PW Duin, et al. “Feedforward neural networks with random weights”. In: 1992 11th IAPR International Conference on Pattern Recognition. IEEE.
1992, pp. 1–4
Braake et al. “Random Activation Weight Neural Net (RAWN) for Fast Non-iterative Training”, EAAI, Elsevier, 1995.
with bias, with direct link:
[RVFL] Yoh-Han Pao, Gwang-Hoon Park, and Dejan J Sobajic. “Learning and generalization characteristics of the random vector functional-link net”. In: Neurocomputing 6.2 (1994), pp. 163–180.
No bias, no direct link (Extreme Learn Machine):
Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. "Extreme learning machine: a new learning scheme of feedforward neural networks." Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on. Vol. 2. IEEE, 2004.
with direct link, no bias:
Zhang, Le, and Ponnuthurai N. Suganthan. "A comprehensive evaluation of random vector functional link networks." Information sciences 367 (2016): 1094-1105.
Cite As
Le Zhang (2026). RVFL_train_val(trainX,trainY,testX,testY,option) (https://www.mathworks.com/matlabcentral/fileexchange/65299-rvfl_train_val-trainx-trainy-testx-testy-option), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.0 (9.87 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0 | N/A |
|
