gpdcTrain: GPD Classifier - training

In evtclass: Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers

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
[e-mail]

References

Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.

See Also

gpdcTest

Examples

trainset <- LETTER[1:15000,]
knowns <- trainset[trainset$class==1, -1]
gpdClassifier <- gpdcTrain(train = knowns, k = 10)

evtclass documentation built on May 2, 2019, 4:16 a.m.