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Multi-modal pattern recognition must frequently truncate the set of initially available modalities. When a kernel-based approach is adopted within each modality, the problem of modality selection becomes mathematically analogous to that of wrapper-based feature selection. In this paper, we revise two implicitly wrapper based methods of SVM-embedded selective kernel combination, the Relevance and Support Kernel Machines, so as to equip them with the ability to preset the desired level of feature-selectivity. Hence, a continuous axis of nested feature selection models is obtained, ranging from the absence of selectivity to the selection of single features. We thus unite the distinct processes of selection and classification within the two techniques in manner suitable for general application within Kernel-based multi-modal pattern recognition.