gensvm.refit | R Documentation |
This function can be used to train an existing model on new data or fit an existing model with slightly different parameters. It is useful for retraining without having to copy all the parameters over. One common application for this is to refit the best model found by a grid search, as illustrated in the examples.
gensvm.refit( fit, x, y, p = NULL, lambda = NULL, kappa = NULL, epsilon = NULL, weights = NULL, kernel = NULL, gamma = NULL, coef = NULL, degree = NULL, kernel.eigen.cutoff = NULL, max.iter = NULL, verbose = NULL, random.seed = NULL )
fit |
Fitted |
x |
Data matrix of the new data |
y |
Label vector of the new data |
p |
if NULL use the value from |
lambda |
if NULL use the value from |
kappa |
if NULL use the value from |
epsilon |
if NULL use the value from |
weights |
if NULL use the value from |
kernel |
if NULL use the value from |
gamma |
if NULL use the value from |
coef |
if NULL use the value from |
degree |
if NULL use the value from |
kernel.eigen.cutoff |
if NULL use the value from |
max.iter |
if NULL use the value from |
verbose |
if NULL use the value from |
random.seed |
if NULL use the value from |
a new fitted gensvm
model
Gerrit J.J. van den Burg, Patrick J.F. Groenen
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
Van den Burg, G.J.J. and Groenen, P.J.F. (2016). GenSVM: A Generalized Multiclass Support Vector Machine, Journal of Machine Learning Research, 17(225):1–42. URL https://jmlr.org/papers/v17/14-526.html.
gensvm
, gensvm-package
x <- iris[, -5] y <- iris[, 5] # fit a standard model and refit with slightly different parameters fit <- gensvm(x, y) fit2 <- gensvm.refit(fit, x, y, epsilon=1e-8) # refit a model returned by a grid search grid <- gensvm.grid(x, y) fit <- gensvm.refit(fit, x, y, epsilon=1e-8) # refit on different data idx <- runif(nrow(x)) > 0.5 x1 <- x[idx, ] x2 <- x[!idx, ] y1 <- y[idx] y2 <- y[!idx] fit1 <- gensvm(x1, y1) fit2 <- gensvm.refit(fit1, x2, y2)
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