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

Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension