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This function builds a regression model using Support Vector Machine.
SVR( x, y, gamma = 2^(-3:3), cost = 2^(-3:3), kernel = c("radial", "linear"), epsilon = c(0.1, 0.5, 1), params = NULL, tune = FALSE, ... )
The classification model.
Predictor matrix.
matrix
Response vector.
vector
The gamma parameter (if a vector, cross-over validation is used to chose the best size).
The cost parameter (if a vector, cross-over validation is used to chose the best size).
The kernel type.
The epsilon parameter (if a vector, cross-over validation is used to chose the best size).
Object containing the parameters. If given, it replaces epsilon, gamma and cost.
epsilon
gamma
cost
If true, the function returns paramters instead of a classification model.
Other arguments.
svm, SVRl, SVRr
svm
SVRl
SVRr
if (FALSE) { require (datasets) data (trees) SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1) SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1) }
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