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Adaptive online learning for nonstationary problems

  • Siegfried Bös
Part III: Learning: Theory and Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

An adaptation algorithm for online training is examined. For stationary tasks it can reduce the learning rate to reach the best convergence. Instead of simple annealing, it keeps the learning rate flexible, such that it can also adapt to nonstationary tasks. Different tasks, abrupt or gradual changes, and different guidance measures are discussed.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Siegfried Bös
    • 1
  1. 1.Information Representation Lab, FRPRIKENWako-shi, SaitamaJapan

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