| gpdcTrain {evtclass} | R Documentation |
GPD Classifier - training
Description
This function is used to train a GPD classifier. It can be used to perform open set classification based on the generalized Pareto distribution.
Usage
gpdcTrain(train, k)
Arguments
train |
a data matrix containing the train data. Class labels should not be included. |
k |
the number of upper order statistics to be used. |
Details
For details on the method and parameters see Vignotto and Engelke (2018).
Value
A list of three elements.
pshapes |
the estimated rescaled shape parameters for each point in the training dataset. |
balls |
the estimated radius for each point in the training dataset. |
k |
the number of upper order statistics used. |
Note
Data are not scaled internally; any preprocessing has to be done externally.
Author(s)
Edoardo Vignotto
edoardo.vignotto@unige.ch
References
Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.
See Also
Examples
trainset <- LETTER[1:15000,]
knowns <- trainset[trainset$class==1, -1]
gpdClassifier <- gpdcTrain(train = knowns, k = 10)