sym.nnet: Symbolic neural networks regression

In RSDA: R to Symbolic Data Analysis

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
hidden	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

RSDA documentation built on Nov. 10, 2023, 5:06 p.m.