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Potential Functions for Signals and Symbolic Sequenceszed probabilistic featureless SVM-based approach to combining different data sources via supervised selective kernel fusion was proposed in our previous papers. In this paper we demonstrate significant qualitative advantages of the proposed approach over other methods of kernel fusion on example of
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One-Class Semi-supervised Learninginear separability in the transformed space of kernel functions. Finally, we examined the work of the proposed algorithm on the USPS dataset and analyzed the relationship of its performance and the size of the initially labeled sample.
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