Dynamic spectrum classification by divergence-based kernel machines and its application to the detection of worn-out banknotes

In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a high-performance classifier. The present paper describes a dynamic spectrum classification method using kernel classifiers with the divergence-based kernel and its application to the detection of worn-out banknotes. We introduce the divergence-based kernel that was proposed as a measure between two probability distributions into the dynamic spectrum classification. The present method is applied to the detection of worn-out banknotes by using acoustic signals for the facilitation of identifying counterfeit banknotes. As a result, the classification performance using the divergence-based kernel is shown to have better performance than those using common kernels such as the Gaussian kernel or the polynomial kernel.