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Many machine learning problems in natural language processing, transaction-log analysis, or computational biology, require the analysis of variable-length sequences, or, more generally, distributions of variablelength sequences. Kernel methods introduced for fixed-size vectors have proven very successful in a variety of machine learning tasks. We recently introduced a new and general kernel framework, rational kernels, to extend these methods to the analysis of variable-length sequences or moredoi:10.1007/s10994-005-0919-8 fatcat:utyzlfhtjncnzon4epc6ctfyay