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We provide preliminary details and formulation of an optimization strategyunder current development that is able to automatically tune the parameters ofa Support Vector Machine over new datasets. The optimization strategy is aheuristic based on Iterated Local Search, a modification of classic hillclimbing which iterates calls to a local search routine.

Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines