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Dynamic ranking learning problem is considered when the training sample is a data stream, consisting of a sequence of a series of objects characterized by a set of features and relative ranks within each series. The problem is reduced to preference learning to rank on clusters in the feature space of ranked objects, while aggregated training dataset is formed from the centers of clusters and estimates...
Reconstruction problem for signals generated by discrete nonlinear dynamic system is considered via unified approach to recurrent kernel-based dynamic systems. In order to prevent the model complexity increasing under on-line identification, the reduced order model kernel method is proposed and proper recurrent Least-Square identification algorithms are designed along with conventional regularization...
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