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A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p ≥ 2 for a set of linearly separable data. Our algorithm, called ALMA_p (Approximate Large Margin algorithm w.r.t. norm p), takes O( (p-1) / (α2 γ2 ) ) corrections to separate the data with p-norm margin larger than (1-α)γ, where g is the (normalized) p-norm margin of the data. ALMA_p avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both real-world and artificial datasets. We compared ALMA_2 (i.e., ALMA_p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by ALMA_2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, ALMA_2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, ALMA_p with p 2 largely outperforms Perceptron-like algorithms, such as ALMA_2.