Abstract
The Support Kernel Machine (SKM) and the Relevance Kernel Machine (RKM) are two principles for selectively combining object-representation modalities of different kinds by means of incorporating supervised selectivity into the classical kernel-based SVM. The former principle consists in rigidly selecting a subset of presumably informative support kernels and excluding the others, whereas the latter one assigns positive weights to all of them. The RKM algorithm was fully elaborated in previous publications; however the previous algorithm implementing the SKM principle of selectivity supervision is applicable only to real-valued features. The present paper fills in this gap by harnessing the framework of subdifferential calculus for computationally solving the problem of constrained nondifferentiable convex optimization that occurs in the SKM training criterion applicable to arbitrary kernel-based modalities of object representation.
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Tatarchuk, A., Urlov, E., Mottl, V., Windridge, D. (2010). A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_17
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DOI: https://doi.org/10.1007/978-3-642-12127-2_17
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