sym.svm: Symbolic Support Vector Machines Regression

In PROMiDAT/RSDA: R to Symbolic Data Analysis

Symbolic Support Vector Machines Regression

Description

Symbolic Support Vector Machines Regression

Usage

sym.svm(
  formula,
  sym.data,
  method = c("cm", "crm"),
  scale = TRUE,
  kernel = "radial"
)

Arguments

formula	a symbolic description of the model to be fit.
sym.data	symbolic data.table
method	method
scale	A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.
kernel	the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type.

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

PROMiDAT/RSDA documentation built on Sept. 14, 2023, 9:16 p.m.