gensvm: Fit the GenSVM model

In gensvm: A Generalized Multiclass Support Vector Machine

Fit the GenSVM model

Description

Fits the Generalized Multiclass Support Vector Machine model with the given parameters. See the package documentation (gensvm-package) for more general information about GenSVM.

Usage

gensvm(
  x,
  y,
  p = 1,
  lambda = 1e-08,
  kappa = 0,
  epsilon = 1e-06,
  weights = "unit",
  kernel = "linear",
  gamma = "auto",
  coef = 1,
  degree = 2,
  kernel.eigen.cutoff = 1e-08,
  verbose = FALSE,
  random.seed = NULL,
  max.iter = 1e+08,
  seed.V = NULL
)

Arguments

x	data matrix with the predictors. Note that for SVMs categorical features should be converted to binary dummy features. This can be done with using the model.matrix function (i.e. model.matrix( ~ var - 1)).
y	class labels
p	parameter for the L_p norm of the loss function (1.0 <= p <= 2.0)
lambda	regularization parameter for the loss function (lambda > 0)
kappa	parameter for the hinge function in the loss function (kappa > -1.0)
epsilon	Stopping parameter for the optimization algorithm. The optimization will stop if the relative change in the loss function is below this value.
weights	type or vector of instance weights to use. Options are 'unit' for unit weights and 'group' for group size correction weights (eq. 4 in the paper). Alternatively, a vector of weights can be provided.
kernel	the kernel type to use in the classifier. It must be one of 'linear', 'poly', 'rbf', or 'sigmoid'. See the section "Kernels in GenSVM" in gensvm-package for more info.
gamma	kernel parameter for the rbf, polynomial, and sigmoid kernel. If gamma is 'auto', then 1/n_features will be used.
coef	parameter for the polynomial and sigmoid kernel.
degree	parameter for the polynomial kernel
kernel.eigen.cutoff	Cutoff point for the reduced eigendecomposition used with kernel-GenSVM. Eigenvectors for which the ratio between their corresponding eigenvalue and the largest eigenvalue is smaller than this cutoff value will be dropped.
verbose	Turn on verbose output and fit progress
random.seed	Seed for the random number generator (useful for reproducible output)
max.iter	Maximum number of iterations of the optimization algorithm.
seed.V	Matrix to warm-start the optimization algorithm. This is typically the output of coef(fit). Note that this function will silently drop seed.V if the dimensions don't match the provided data.

Value

A "gensvm" S3 object is returned for which the print, predict, coef, and plot methods are available. It has the following items:

call	The call that was used to construct the model.
p	The value of the lp norm in the loss function
lambda	The regularization parameter used in the model.
kappa	The hinge function parameter used.
epsilon	The stopping criterion used.
weights	The instance weights type used.
kernel	The kernel function used.
gamma	The value of the gamma parameter of the kernel, if applicable
coef	The value of the coef parameter of the kernel, if applicable
degree	The degree of the kernel, if applicable
kernel.eigen.cutoff	The cutoff value of the reduced eigendecomposition of the kernel matrix.
verbose	Whether or not the model was fitted with progress output
random.seed	The random seed used to seed the model.
max.iter	Maximum number of iterations of the algorithm.
n.objects	Number of objects in the dataset
n.features	Number of features in the dataset
n.classes	Number of classes in the dataset
classes	Array with the actual class labels
V	Coefficient matrix
n.iter	Number of iterations performed in training
n.support	Number of support vectors in the final model
training.time	Total training time

Note

This function returns partial results when the computation is interrupted by the user.

Author(s)

Gerrit J.J. van den Burg, Patrick J.F. Groenen
Maintainer: Gerrit J.J. van den Burg <[e-mail]>

References

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.

See Also

coef, print, predict, plot, gensvm.grid, gensvm-package

Examples

x <- iris[, -5]
y <- iris[, 5]

# fit using the default parameters and show progress
fit <- gensvm(x, y, verbose=TRUE)

# fit with some changed parameters
fit <- gensvm(x, y, lambda=1e-6)

# Early stopping defined through epsilon
fit <- gensvm(x, y, epsilon=1e-3)

# Early stopping defined through max.iter
fit <- gensvm(x, y, max.iter=1000)

# Nonlinear training
fit <- gensvm(x, y, kernel='rbf', max.iter=1000)
fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=1000)

# Setting the random seed and comparing results
fit <- gensvm(x, y, random.seed=123, max.iter=1000)
fit2 <- gensvm(x, y, random.seed=123, max.iter=1000)
all.equal(coef(fit), coef(fit2))

gensvm documentation built on Feb. 16, 2023, 5:58 p.m.