# Rule from SVM results

I am using Support Vector Machines from "e1071" package in R. For standard IRIS data the code is:

data(iris);
attach(iris);
x <- subset(iris, select = -Species);
y <- Species;
model <- svm(x, y)


After that using function predict() I can get a prediction with the trained model for any data from the test Set.

However the question for me is: ow can I get the real rule of finding a class for samples from Test Set?

Support Vector Machines determines high-dimensional hyperplane which divides data into classes. From literature I found that classifier (hypersurface) is in the simplest case defined by weights (W) and parameter (B). However for nonlinear cases one need transformation $x \rightarrow f_{i}(x)$.

Can anyone give any example of extracting the real rule from any package with SVM (preferably in R)?