% 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)
 }
 }
 }
}

\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{https://jmlr.org/papers/v17/14-526.html}.
}
\seealso{
\code{\link{gensvm}}, \code{\link{gensvm.grid}}
}
\author{
Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
}