# NOT RUN {
library(kernlab)
library(caret)
library(RMKL)
#Load data
data(benchmark.data)
example.data=benchmark.data[[1]]
# Split samples into training and test sets
training.samples=sample(1:dim(example.data)[1],floor(0.7*dim(example.data)[1]),replace=FALSE)
# Set up cost parameters and kernels
C=100
kernels=rep('radial',3)
degree=rep(0,3)
scale=rep(0,3)
sigma=c(0,2^seq(-3:0))
K=kernels.gen(example.data[,1:2], training.samples, kernels, degree, scale, sigma)
K.train=K$K.train
SimpleMKL.classification(K.train,example.data[training.samples,3], C)
# }
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