  The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning
machine, and a low VC dimension leads to good generalization. The recently
proposed Minimal Complexity Machine (MCM) learns a hyperplane classifier by
minimizing an exact bound on the VC dimension. This paper extends the MCM
classifier to the fuzzy domain. The use of a fuzzy membership is known to
reduce the effect of outliers, and to reduce the effect of noise on learning.
Experimental results show, that on a number of benchmark datasets, the the
fuzzy MCM classifier outperforms SVMs and the conventional MCM in terms of
generalization, and that the fuzzy MCM uses fewer support vectors. On several
benchmark datasets, the fuzzy MCM classifier yields excellent test set
accuracies while using one-tenth the number of support vectors used by SVMs.
