```
# NOT RUN {
## simple example using the spam data set
data(spam)
## create test and training set
index <- sample(1:dim(spam)[1])
spamtrain <- spam[index[1:floor(dim(spam)[1]/2)], ]
spamtest <- spam[index[((ceiling(dim(spam)[1]/2)) + 1):dim(spam)[1]], ]
## train a support vector machine
filter <- ksvm(type~.,data=spamtrain,kernel="rbfdot",
kpar=list(sigma=0.05),C=5,cross=3)
filter
## predict mail type on the test set
mailtype <- predict(filter,spamtest[,-58])
## Check results
table(mailtype,spamtest[,58])
## Another example with the famous iris data
data(iris)
## Create a kernel function using the build in rbfdot function
rbf <- rbfdot(sigma=0.1)
rbf
## train a bound constraint support vector machine
irismodel <- ksvm(Species~.,data=iris,type="C-bsvc",
kernel=rbf,C=10,prob.model=TRUE)
irismodel
## get fitted values
fitted(irismodel)
## Test on the training set with probabilities as output
predict(irismodel, iris[,-5], type="probabilities")
## Demo of the plot function
x <- rbind(matrix(rnorm(120),,2),matrix(rnorm(120,mean=3),,2))
y <- matrix(c(rep(1,60),rep(-1,60)))
svp <- ksvm(x,y,type="C-svc")
plot(svp,data=x)
### Use kernelMatrix
K <- as.kernelMatrix(crossprod(t(x)))
svp2 <- ksvm(K, y, type="C-svc")
svp2
# test data
xtest <- rbind(matrix(rnorm(20),,2),matrix(rnorm(20,mean=3),,2))
# test kernel matrix i.e. inner/kernel product of test data with
# Support Vectors
Ktest <- as.kernelMatrix(crossprod(t(xtest),t(x[SVindex(svp2), ])))
predict(svp2, Ktest)
#### Use custom kernel
k <- function(x,y) {(sum(x*y) +1)*exp(-0.001*sum((x-y)^2))}
class(k) <- "kernel"
data(promotergene)
## train svm using custom kernel
gene <- ksvm(Class~.,data=promotergene[c(1:20, 80:100),],kernel=k,
C=5,cross=5)
gene
#### Use text with string kernels
data(reuters)
is(reuters)
tsv <- ksvm(reuters,rlabels,kernel="stringdot",
kpar=list(length=5),cross=3,C=10)
tsv
## regression
# create data
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.03)
# train support vector machine
regm <- ksvm(x,y,epsilon=0.01,kpar=list(sigma=16),cross=3)
plot(x,y,type="l")
lines(x,predict(regm,x),col="red")
# }
```

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