Fuzzy support vector machine (FSVM) classifiers are a class of nonlinear binary classifiers which extend Vapnik's support vector machine (SVM) formulation. In the absence of additional information, fuzzy membership values are usually selected based on the distribution of training vectors, where a number of assumptions are made about the underlying shape of this distribution. In this paper we present an alternative method of generating membership values which we call iterative FSVM (I-FSVM). Our method generates membership values iteratively based on the positions of training vectors relative to the SVM decision surface itself. We show that our algorithm is capable of generating results equivalent to an SVM with a modified (non distance based) penalty (risk) function. Experiments have been carried out on three real world binary classification problems taken from the UCI repository, namely the spambase dataset and the adult (census) dataset.