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GenSVM R Package
================
<!-- badges: start -->
[](https://travis-ci.org/GjjvdBurg/RGenSVM)
<!-- badges: end -->
This package implements the GenSVM Multiclass Support Vector Machine
classifier in R.
```r
# Plot created with:
> library(gensvm)
> x <- iris[, -5]
> y <- iris[, 5]
> fit <- gensvm(x, y, kernel='rbf', gamma=10, max.iter=5000, verbose=1, random.seed=123)
> plot(fit, xlim=c(-5, 5), ylim=c(-5, 5))
> title("Iris dataset (GenSVM + RBF)")
```
Introduction
------------
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.
For more information about GenSVM, see the paper: [GenSVM: A Generalized
Multiclass Support Vector Machine](https://jmlr.org/papers/v17/14-526.html) by
G.J.J. van den Burg and P.J.F. Groenen (*Journal of Machine Learning
Research*, 2016).
Installation
------------
This package can be installed from CRAN:
```r
> install.packages('gensvm')
```
Usage
-----
The package is extensively documented with many examples. See
`?gensvm-package`, `?gensvm` and `?gensvm.grid` in R.
The main GenSVM functions are:
* `gensvm` : Fit a GenSVM model for specific model parameters.
* `gensvm.grid` : Run a cross-validated grid search for GenSVM.
Both these functions return S3 objects for which `plot` and `predict`
functions are available. For the GenSVMGrid object the function is applied to
the best model found during training. For both of these objects a `coef`
function is also available.
The following utility functions are also included in the package:
* `gensvm.accuracy` : Compute the accuracy score between true and predicted
class labels
* `gensvm.maxabs.scale` : Scale each column of the dataset by its maximum
absolute value, preserving sparsity and mapping the data to [-1, 1]
* `gensvm.train.test.split` : Split a dataset into a training and testing
sample
* `gensvm.refit` : Refit a fitted GenSVM model with slightly different
parameters or on a different dataset
Citing
------
If you use GenSVM in your work, please cite the paper using the information
available through the following R command:
```r
> citation('gensvm')
```
Alternatively, you can use the following BibTeX code directly:
```bib
@article{JMLR:v17:14-526,
author = {Gerrit J.J. {van den Burg} and Patrick J.F. Groenen},
title = {{GenSVM}: A Generalized Multiclass Support Vector Machine},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {225},
pages = {1-42},
url = {https://jmlr.org/papers/v17/14-526.html}
}
```
License
-------
Copyright 2018, G.J.J. van den Burg.
RGenSVM is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
RGenSVM is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with RGenSVM. If not, see <http://www.gnu.org/licenses/>.
For more information please contact:
G.J.J. van den Burg
email: gertjanvandenburg@gmail.com
|
