Fast Heuristics For The Estimation Of the C Constant Of A Support Vector Machine.

heuristicC implements a heuristics proposed by Thorsten Joachims in order to make fast estimates of a convenient value for the C constant used by support vector machines. This implementation only works for linear support vector machines.

heuristicC(data)

Arguments

data

a nxp data matrix. Each row stands for an example (sample, point) and each column stands for a dimension (feature, variable)

Value

A value for the C constant is returned, computed as follows:
\(\frac{1}{\frac{1}{n}\sum_{i=1}^{n}\sqrt{G[i,i]}}\) where \(G=\code{data}\%*\%t(\code{data})\)

Note

Classification models usually perform better if each dimension of the data is first centered and scaled. If data are scaled, it is better to compute the heuristics on the scaled data as well.

References

See also

LiblineaR

Author

Thibault Helleputte thibault.helleputte@dnalytics.com

Examples

data(iris)

x=iris[,1:4]
y=factor(iris[,5])
train=sample(1:dim(iris)[1],100)

xTrain=x[train,]
xTest=x[-train,]
yTrain=y[train]
yTest=y[-train]

# Center and scale data
s=scale(xTrain,center=TRUE,scale=TRUE)

# Sparse Logistic Regression
t=6

co=heuristicC(s)
m=LiblineaR(data=s,labels=yTrain,type=t,cost=co,bias=TRUE,verbose=FALSE)
#> Warning: Argument 'labels' is deprecated and was renamed to 'target'.