#' GenSVM: A Generalized Multiclass Support Vector Machine #' #' The GenSVM classifier is a generalized multiclass support vector machine #' (SVM). This classifier simultaneously aims to find decision boundaries that #' separate the classes with as wide a margin as possible. In GenSVM, the loss #' functions that measures how misclassifications are counted is very flexible. #' This allows the user to tune the classifier to the dataset at hand and #' potentially obtain higher classification accuracy. Moreover, this #' flexibility means that GenSVM has a number of alternative multiclass SVMs as #' special cases. One of the other advantages of GenSVM is that it is trained #' in the primal, allowing the use of warm starts during optimization. This #' means that for common tasks such as cross validation or repeated model #' fitting, GenSVM can be trained very quickly. #' #' This package provides functions for training the GenSVM model either as a #' separate model or through a cross-validated parameter grid search. In both #' cases the GenSVM C library is used for speed. Auxiliary functions for #' evaluating and using the model are also provided. #' #' @section GenSVM functions: #' The main GenSVM functions are: #' \describe{ #' \item{\code{\link{gensvm}}}{Fit a GenSVM model for specific model #' parameters.} #' \item{\code{\link{gensvm.grid}}}{Run a cross-validated grid search for #' GenSVM.} #' } #' #' Other available functions are: #' \describe{ #' \item{\code{\link{plot}}}{Plot the low-dimensional \emph{simplex} space #' where the decision boundaries are fixed.} #' \item{\code{\link{predict}}}{Predict the class labels of new data using the #' GenSVM model.} #' \item{\code{\link{coef}}}{Get the coefficients of the GenSVM model} #' \item{\code{\link{print}}}{Print a short description of the fitted GenSVM #' model} #' } #' #' @author #' Gerrit J.J. van den Burg, Patrick J.F. Groenen #' Maintainer: Gerrit J.J. van den Burg #' #' @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, #' 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}. #' #' @examples #' #' #' @name gensvm-package #' @docType package #' @import NULL #>NULL