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Logistic regression is presumably the most popular representative of probabilistic discriminative classifiers. In this paper, a kernel variant of logistic regression is introduced as an iteratively re-weighted least-squares algorithm in kernel-induced feature spaces. This formulation allows us to apply highly efficient approximation methods that are capable of dealing with large-scale problems. For multi-class problems, a pairwise coupling procedure is proposed. Pairwise coupling for "kernelized" logistic regression effectively overcomes conceptual and numerical problems of standard multi-class kernel classifiers.