predict.gausspr: predict method for Gaussian Processes object

In elad663/kernlab: Kernel-Based Machine Learning Lab

Description

Prediction of test data using Gaussian Processes

Usage

## S4 method for signature 'gausspr'
predict(object, newdata, type = "response", coupler = "minpair")

Arguments

object	an S4 object of class gausspr created by the gausspr function
newdata	a data frame or matrix containing new data
type	one of response, probabilities indicating the type of output: predicted values or matrix of class probabilities
coupler	Coupling method used in the multiclass case, can be one of minpair or pkpd (see reference for more details).

Value

response	predicted classes (the classes with majority vote) or the response value in regression.
probabilities	matrix of class probabilities (one column for each class and one row for each input).

Author(s)

Alexandros Karatzoglou
[e-mail]

References

• C. K. I. Williams and D. Barber
  Bayesian classification with Gaussian processes.
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998
  http://www.dai.ed.ac.uk/homes/ckiw/postscript/pami_final.ps.gz

• T.F. Wu, C.J. Lin, R.C. Weng.
  Probability estimates for Multi-class Classification by Pairwise Coupling
  http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf

Examples

## example using the promotergene data set
data(promotergene)

## create test and training set
ind <- sample(1:dim(promotergene)[1],20)
genetrain <- promotergene[-ind, ]
genetest <- promotergene[ind, ]

## train a support vector machine
gene <- gausspr(Class~.,data=genetrain,kernel="rbfdot",
                kpar=list(sigma=0.015))
gene

## predict gene type probabilities on the test set
genetype <- predict(gene,genetest,type="probabilities")
genetype

elad663/kernlab documentation built on May 7, 2019, 6:06 a.m.