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We propose a new-kernel based approach for linear system identification. The impulse response is modeled as realization of a Gaussian process which includes information on smoothness and BIBO-stability. The corresponding minimum- variance estimate belongs to a Reproducing kernel Hilbert space which is given a spectral characterization and shown to be dense in the space of continuous functions. The...
Recently, standard single-task kernel methods have been extended to the case of multi-task learning under the framework of regularization. Experimental results have shown that such an approach can perform much better than single-task techniques, especially when few examples per task are available. However, a possible drawback may be computational complexity. For instance, when using regularization...
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