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A Note on Parameter Tuning for On-Line Shifting Algorithms

2003

Technical Report

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In this short note, building on ideas of M. Herbster [2] we propose a method for automatically tuning the parameter of the FIXED-SHARE algorithm proposed by Herbster and Warmuth [3] in the context of on-line learning with shifting experts. We show that this can be done with a memory requirement of $O(nT)$ and that the additional loss incurred by the tuning is the same as the loss incurred for estimating the parameter of a Bernoulli random variable.

Author(s): Bousquet, O.
Year: 2003
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@techreport{2294,
  title = {A Note on Parameter Tuning for On-Line Shifting Algorithms},
  author = {Bousquet, O.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, T{\"u}bingen, Germany},
  school = {Biologische Kybernetik},
  year = {2003}
}