heuristicC: Fast Heuristics For The Estimation Of the C Constant Of A...

In LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library

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

Description

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.

Usage

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). A vector will be transformed to a n x 1 matrix. Sparse matrices of class matrix.csr, matrix.csc and matrix.coo from package SparseM are accepted, as are dgCMatrix, dgRMatrix and dgTMatrix from package Matrix – the same classes accepted by LiblineaR and predict.LiblineaR.

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.

Author(s)

Thibault Helleputte [e-mail]

References

• T. Joachims
  SVM light (2002)
  http://svmlight.joachims.org

See Also

LiblineaR

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,target=yTrain,type=t,cost=co,bias=TRUE,verbose=FALSE)

LiblineaR documentation built on Sept. 24, 2026, 5:11 p.m.