R/gensvm-package.R

In gensvm: A Generalized Multiclass Support Vector Machine

#' GenSVM: A Generalized Multiclass Support Vector Machine
#'
#' 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.
#'
#' 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{https://jmlr.org/papers/v17/14-526.html}.
#'
#' @seealso
#' \code{\link{gensvm}}, \code{\link{gensvm.grid}}
#'
#' @aliases
#' gensvm.package
#'
#' @name gensvm-package
#' @docType package
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