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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gensvm-package.R
+\docType{package}
+\name{gensvm-package}
+\alias{gensvm-package}
+\alias{gensvm.package}
+\title{GenSVM: A Generalized Multiclass Support Vector Machine}
+\description{
+The GenSVM classifier is a generalized multiclass support vector machine 
+(SVM). This classifier 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 space, 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.
+}
+\details{
+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.}
+}
+
+For the GenSVM and GenSVMGrid models the following two functions are 
+available. When applied to a GenSVMGrid object, the function is applied to 
+the best GenSVM model.
+\describe{
+\item{\code{\link{plot}}}{Plot the low-dimensional \emph{simplex} space 
+where the decision boundaries are fixed (for problems with 3 classes).}
+\item{\code{\link{predict}}}{Predict the class labels of new data using the 
+GenSVM model.}
+}
+
+Moreover, for the GenSVM and GenSVMGrid models a \code{coef} function is 
+defined:
+\describe{
+\item{\code{\link{coef.gensvm}}}{Get the coefficients of the fitted GenSVM 
+model.}
+\item{\code{\link{coef.gensvm.grid}}}{Get the parameter grid of the GenSVM 
+grid search.}
+}
+
+The following utility functions are also included:
+\describe{
+\item{\code{\link{gensvm.accuracy}}}{Compute the accuracy score between true 
+and predicted class labels}
+\item{\code{\link{gensvm.maxabs.scale}}}{Scale each column of the dataset by 
+its maximum absolute value, preserving sparsity and mapping the data to [-1, 
+1]}
+\item{\code{\link{gensvm.train.test.split}}}{Split a dataset into a training 
+and testing sample}
+\item{\code{\link{gensvm.refit}}}{Refit a fitted GenSVM model with slightly 
+different parameters or on a different dataset}
+}
+}
+
+\section{Kernels in GenSVM}{
+
+
+GenSVM can be used for both linear and nonlinear multiclass support vector 
+machine classification. In general, linear classification will be faster but 
+depending on the dataset higher classification performance can be achieved 
+using a nonlinear kernel.
+
+The following nonlinear kernels are implemented in the GenSVM package:
+\describe{
+ \item{RBF}{The Radial Basis Function kernel is a well-known kernel function 
+ based on the Euclidean distance between objects. It is defined as
+ \deqn{
+     k(x_i, x_j) = exp( -\gamma || x_i - x_j ||^2 )
+     }
+     }
+ \item{Polynomial}{A polynomial kernel can also be used in GenSVM. This 
+ kernel function is implemented very generally and therefore takes three 
+ parameters (\code{coef}, \code{gamma}, and \code{degree}). It is defined 
+ as:
+ \deqn{
+     k(x_i, x_j) = ( \gamma x_i' x_j + coef)^{degree}
+ }
+ }
+ \item{Sigmoid}{The sigmoid kernel is the final kernel implemented in 
+ GenSVM. This kernel has two parameters and is implemented as follows:
+ \deqn{
+     k(x_i, x_j) = \tanh( \gamma x_i' x_j + coef)
+ }
+ }
+ }
+}
+\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, 
+17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
+}
+