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)


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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  1. 1.
    Amari S. (1997), in NIPS 9, MIT Press, in press.Google Scholar
  2. 2.
    Opper M. (1996), Phys. Rev. Lett. 77, p. 4671–4674.Google Scholar
  3. 3.
    Sompolinsky H., Barkai N. & Seung H.S. (1995), in Neural Networks: The Statistical Mechanics Perspective, World Scientific, p. 105–130.Google Scholar
  4. 4.
    Bös S. (1995), in ICANN'95, p. 111-116, and to be submitted.Google Scholar
  5. 5.
    Bös S. (1996), in ICANN'96, Springer LNCS 1112, p. 89–94.Google Scholar
  6. 6.
    Bös S., Murata N., Amari S., & Müller K.-R. (1997), submitted.Google Scholar
  7. 7.
    Murata N., Bös S., Amari S., & Müller K.-R. (1997), in preparation.Google Scholar
  8. 8.
    Murata N., Müller K.-R., Ziehe A., & Amari S. (1997), in NIPS 9, MIT Press, in press.Google Scholar

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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