Online Learning with Multiple Operator-valued Kernels

Abstract : We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernel-based online learning algorithm NORMA from scalar-valued to operator-valued setting. We report a cumulative error bound that holds both for classification and regression. We then define a second algorithm, MONORMA, which addresses the limitation of pre-defining the output structure in ONORMA by learning sequentially a linear combination of operator-valued kernels. Our experiments show that the proposed algorithms achieve good performance results with low computational cost.
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Pré-publication, Document de travail
2013
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Contributeur : Julien Audiffren <>
Soumis le : mardi 5 novembre 2013 - 16:10:20
Dernière modification le : mercredi 21 mars 2018 - 15:28:02
Document(s) archivé(s) le : vendredi 7 avril 2017 - 21:47:23

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  • HAL Id : hal-00879148, version 2
  • ARXIV : 1311.0222

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Julien Audiffren, Hachem Kadri. Online Learning with Multiple Operator-valued Kernels. 2013. 〈hal-00879148v2〉

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