Abstract
In this study, we propose a supervised learning problem to differentiate genuine from fake banknotes. The problem is reformulated as a regularized optimization problem whose fidelity term is the hinge loss function and the hypothesis space is constructed by a reproducing kernel Hilbert space (RKSH). The numerical results are realized by the Adam method, which shows her success in detecting counterfeit banknotes compared to the stochastic gradient descent (SGD) algorithm.
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