sym.nnet {RSDA} | R Documentation |
Symbolic neural networks regression
Description
Symbolic neural networks regression
Usage
sym.nnet(
formula,
sym.data,
method = c("cm", "crm"),
hidden = c(10),
threshold = 0.05,
stepmax = 1e+05
)
Arguments
formula |
a symbolic description of the model to be fitted. |
sym.data |
symbolic data.table |
method |
cm crm |
a vector of integers specifying the number of hidden neurons (vertices) in each layer. | |
threshold |
a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria. |
stepmax |
the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38