A stretchy classification methodology adopting multivariate polynomials is proposed in this paper. Through minimization of an approximated p-norm of the parameter vector subject to classification error constraints, an approximated minimum norm solution in dual form is derived for under-determined systems. This is subsequently transformed into its primal form for over-determined systems. Practical feasibility of the proposed solution is illustrated by an evaluation on synthetic data as well as an application on benchmark real-world data.
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