Description Usage Arguments Value
add_neurons
adds a specific number of hidden neurons to the ELM being
all of them of the same type of activation function.
1 | add_neurons(object, ...)
|
object |
An instance to the ELM class. |
nn |
The number of hidden neurons to add to the network. |
act_fun |
The activation function of the added neurons. Several types:
|
w_in |
An input weight matrix of dimension [dxL]. List of centroids for rbf activation functions. |
b |
An input bias vector of dimension [1xL]. Vector of sigmas for rbf activation functions. |
An object ELM with new neurons added and w_in and b matrices updated.
It is called by the training wrapper when a new ELM object is created. It is called sequentially based on the different type of activation functions.
When addNeurons is called explicitly, the ELM should be re-trained
For linear activation functions, the number of neurons added cannot be superior to the number of features (L=<d). This case entails a linear projection of data to a higher dimensional, which yields a multicorrelated new space.
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