ELM is a kind of Feedforward Neuron Network with only a single hidden layer. With the randomly selected input weight (w, b), the actual learning part of the network is the output weight β for supervised learning.
In actual training, there are 2 most important parameters you should judge:
- the number of neurons of hidden layer
L - the regularization parameter
Cof ridge regression
There are some good material for learning the theory and the code of ELM, which I used:
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Most helpful article! It introduces the basic knowledge of mathematical about Moore-Penrose and minimum norm Least-Squares solution at first. Then explain the algorithm pretty clear!!
极限学习机简介 -
Simple and clear explanation with short but enough code:
Extreme Learning Machines 极限学习机 -
Pretty detail about the computation of
β, explain the disadvantages
of BP and the which situations should use which formula:
极限学习机应用于入侵检测（一） -
Discuss the importance of normalization:
ELM python实现 -
Also a good material for learning:
极限学习机（ELM）从原理到程序实现（附完整代码）
