A Generalized Multiclass Support Vector Machine
c(person("Gertjan", "van den Burg", role=c("aut", "cre"),
email="gertjanvandenburg@gmail.com"),
person("Patrick", "Groenen", email="groenen@ese.eur.nl", role="ctb"))
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 function is very flexible in the way that misclassifications are
penalized.  This allows the user to tune the classifier to the dataset
at hand and potentially obtain higher classification accuracy than
alternative multiclass SVMs.  Moreover, this flexibility means that
GenSVM has a number of other 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. Based on: G.J.J. van den
Burg and P.J.F. Groenen (2018) <http://www.jmlr.org/papers/v17/14-526.html>.
Gertjan van den Burg [aut, cre],
Patrick Groenen [ctb]

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 function is very flexible in the way that misclassifications are
penalized.  This allows the user to tune the classifier to the dataset
at hand and potentially obtain higher classification accuracy than
alternative multiclass SVMs.  Moreover, this flexibility means that
GenSVM has a number of other 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. Based on: G.J.J. van den
Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.