ed_rwnn {RWNN} | R Documentation |
Ensemble deep random weight neural networks
Description
Use multiple layers to create deep ensemble random weight neural network models.
Usage
ed_rwnn(
formula,
data = NULL,
n_hidden,
lambda = 0,
method = NULL,
type = NULL,
control = list()
)
## S3 method for class 'formula'
ed_rwnn(
formula,
data = NULL,
n_hidden,
lambda = 0,
method = NULL,
type = NULL,
control = list()
)
Arguments
formula |
A formula specifying features and targets used to estimate the parameters of the output layer. |
data |
A data-set (either a data.frame or a tibble) used to estimate the parameters of the output layer. |
A vector of integers designating the number of neurons in each of the hidden layers (the length of the list is taken as the number of hidden layers). | |
lambda |
The penalisation constant(s) passed to either rwnn or ae_rwnn (see |
method |
The penalisation type passed to ae_rwnn. Set to |
type |
A string indicating whether this is a regression or classification problem. |
control |
A list of additional arguments passed to the control_rwnn function. |
Value
An ERWNN-object.
References
Shi Q., Katuwal R., Suganthan P., Tanveer M. (2021) "Random vector functional link neural network based ensemble deep learning." Pattern Recognition, 117, 107978.
Examples
n_hidden <- c(20, 15, 10, 5)
lambda <- 0.01
#
m <- ed_rwnn(y ~ ., data = example_data, n_hidden = n_hidden, lambda = lambda)