Symbolic neural networks regression
sym.nnet(
formula,
sym.data,
method = c("cm", "crm"),
hidden = c(10),
threshold = 0.05,
stepmax = 1e+05
)a symbolic description of the model to be fitted.
symbolic data.table
cm crm
a vector of integers specifying the number of hidden neurons (vertices) in each layer.
a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria.
the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.
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