Search for: [Abstrakt = "Within the realm of machine learning, kernel meth\-ods stand out as a prominent class of algorithms with widespreadapplications, including but not limited to classification, regres\-sion, and identification tasks. Our paper addresses the chal\-lenging problem of identifying the finite impulse response \(FIR\)of single\-input single\-output nonlinear systems under the in\-fluence of perturbations and binary\-valued measurements. Toovercome this challenge, we exploit two algorithms that leveragethe framework of reproducing kernel Hilbert spaces \(RKHS\) toaccurately identify the impulse response of the Proakis C chan\-nel. Additionally, we introduce the application of these kernelmethods for estimating binary output data of nonlinear systems.We showcase the effectiveness of kernel adaptive filters in identi\-fying nonlinear systems with binary output measurements, asdemonstrated through the experimental results presented in thisstudy."]

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