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Diffstat (limited to 'man/gensvm.Rd')
| -rw-r--r-- | man/gensvm.Rd | 51 |
1 files changed, 25 insertions, 26 deletions
diff --git a/man/gensvm.Rd b/man/gensvm.Rd index e48444f..b6c9bf0 100644 --- a/man/gensvm.Rd +++ b/man/gensvm.Rd @@ -11,8 +11,8 @@ gensvm(x, y, p = 1, lambda = 1e-08, kappa = 0, epsilon = 1e-06, } \arguments{ \item{x}{data matrix with the predictors. \cr\cr -Note that for SVMs categorical features should be converted to binary dummy -features. This can be done with using the \code{\link{model.matrix}} +Note that for SVMs categorical features should be converted to binary dummy +features. This can be done with using the \code{\link{model.matrix}} function (i.e. \code{model.matrix( ~ var - 1)}).} \item{y}{class labels} @@ -21,15 +21,15 @@ function (i.e. \code{model.matrix( ~ var - 1)}).} \item{lambda}{regularization parameter for the loss function (lambda > 0)} -\item{kappa}{parameter for the hinge function in the loss function (kappa > +\item{kappa}{parameter for the hinge function in the loss function (kappa > -1.0)} -\item{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 +\item{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.} -\item{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" +\item{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 \code{\link{gensvm-package}} for more info.} \item{gamma}{kernel parameter for the rbf, polynomial, and sigmoid kernel. @@ -39,24 +39,24 @@ If gamma is 'auto', then 1/n_features will be used.} \item{degree}{parameter for the polynomial kernel} -\item{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 +\item{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.} \item{verbose}{Turn on verbose output and fit progress} -\item{random.seed}{Seed for the random number generator (useful for +\item{random.seed}{Seed for the random number generator (useful for reproducible output)} \item{max.iter}{Maximum number of iterations of the optimization algorithm.} -\item{seed.V}{Matrix to warm-start the optimization algorithm. This is -typically the output of \code{coef(fit)}. Note that this function will +\item{seed.V}{Matrix to warm-start the optimization algorithm. This is +typically the output of \code{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, +A "gensvm" S3 object is returned for which the print, predict, coef, and plot methods are available. It has the following items: \item{call}{The call that was used to construct the model.} \item{p}{The value of the lp norm in the loss function} @@ -68,7 +68,7 @@ and plot methods are available. It has the following items: \item{gamma}{The value of the gamma parameter of the kernel, if applicable} \item{coef}{The value of the coef parameter of the kernel, if applicable} \item{degree}{The degree of the kernel, if applicable} -\item{kernel.eigen.cutoff}{The cutoff value of the reduced +\item{kernel.eigen.cutoff}{The cutoff value of the reduced eigendecomposition of the kernel matrix.} \item{verbose}{Whether or not the model was fitted with progress output} \item{random.seed}{The random seed used to seed the model.} @@ -83,12 +83,12 @@ eigendecomposition of the kernel matrix.} \item{training.time}{Total training time} } \description{ -Fits the Generalized Multiclass Support Vector Machine model -with the given parameters. See the package documentation +Fits the Generalized Multiclass Support Vector Machine model +with the given parameters. See the package documentation (\code{\link{gensvm-package}}) for more general information about GenSVM. } \note{ -This function returns partial results when the computation is interrupted by +This function returns partial results when the computation is interrupted by the user. } \examples{ @@ -121,17 +121,16 @@ all.equal(coef(fit), coef(fit2)) } -\author{ -Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr -Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com> -} \references{ -Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized -Multiclass Support Vector Machine}, Journal of Machine Learning Research, +Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized +Multiclass Support Vector Machine}, Journal of Machine Learning Research, 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}. } \seealso{ -\code{\link{coef}}, \code{\link{print}}, \code{\link{predict}}, +\code{\link{coef}}, \code{\link{print}}, \code{\link{predict}}, \code{\link{plot}}, \code{\link{gensvm.grid}}, \code{\link{gensvm-package}} } - +\author{ +Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr +Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com> +} |