Extreme Learning Machine for classification and regression

a single hidden layer feed-forward network for regression or classification Trained based on ELM.

Extreme Learning Machine ELM is the new dominate training tool for trainig a single hidden layer feed-forward neural network.
the basic learning rules of ELM is presented In these codes.

Important characteristics of this version:
- It extended for usage for both classification and regression.
- It contains functions that normalize the input samples between any desired values.
For classification:
- It allows encoding of the labels of classes into binary codes to satisfy the constraints of Activation functions boundaries.
- After training and in case of prediction the algorithm has the capability to decode again those codes into original labels.
For regression:
- The algorithm also can renormalize the output values after training into original interval.

For any information concerning this code contact me via : [e-mail]

[1] G. Huang, S. Member, H. Zhou, X. Ding, and R. Zhang, “Extreme Learning Machine for Regression and Multiclass Classification,” vol. 42, no. 2, pp. 513–529, 2012.