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In this contribution, we introduce a new on-line approximate maximal margin learning algorithm based on an extension of the perceptron algorithm. This extension, which we call fixed margin perceptron (FMP), finds the solution of a linearly separable learning problem given a fixed margin. It is shown that this algorithm converges in (R^2-@c"f^2)/(@c^*-@c"f)^2 updates, where @c"f