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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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Preprints, Working Papers, ...
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Contributor : Julien Audiffren Connect in order to contact the contributor
Submitted on : Tuesday, November 5, 2013 - 4:10:20 PM
Last modification on : Friday, October 22, 2021 - 3:33:26 AM
Long-term archiving on: : Friday, April 7, 2017 - 9:47:23 PM


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



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



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