Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download

https://hal.inria.fr/hal-00879148
Contributor : Julien Audiffren <>
Submitted on : Tuesday, November 5, 2013 - 4:10:20 PM
Last modification on : Thursday, January 23, 2020 - 6:22:14 PM
Long-term archiving on: : Friday, April 7, 2017 - 9:47:23 PM

Files

AISTATSOnline.pdf
Files produced by the author(s)

Identifiers

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

Collections

Citation

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

Share

Metrics

Record views

335

Files downloads

349