[BioC] Choosing the kernel for SVM (regression) using rminer package

[Paul [guest]]

In the rminer package by Paulo Cortez, using the mining function, it is possible to do a SVM regression.Using the script from the documentation

SV=mining(V26~.,d,model="svm",Runs=10,method=v,mpar=m,search=s,feat="s")

Is it possible to choose a kernel for the regression other than the default gaussian kernel? I would like to apply the same to a non-linear data and prefer to use a spline or other types of kernel for the same function.

 -- output of sessionInfo(): 

R version 3.0.3 (2014-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252    LC_MONETARY=French_France.1252
[4] LC_NUMERIC=C                   LC_TIME=French_France.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] rminer_1.3.1         BiocInstaller_1.12.0 randomForest_4.6-7  

loaded via a namespace (and not attached):
 [1] grid_3.0.3      igraph_0.7.0    kernlab_0.9-19  kknn_1.2-5      lattice_0.20-27 Matrix_1.1-3    nnet_7.3-8     
 [8] plotrix_3.5-5   rpart_4.1-8     tools_3.0.3    

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