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This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon support vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm is illustrated using extensive experimental results.