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Preprints, Working Papers, ... Year : 2013

Online Learning with Multiple Operator-valued Kernels


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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Dates and versions

hal-00879148 , version 1 (01-11-2013)
hal-00879148 , version 2 (05-11-2013)



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