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Online Learning From Finite Training Sets (1998)  (Make Corrections)  (2 citations)
David Barber, Peter Sollich



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Abstract: We analyse online gradient descent learning from finite training sets at non-infinitesimal learning rates j for both linear and non-linear networks. In the linear case, exact results are obtained for the time-dependent generalization error of networks with a large number of weights N , trained on p = ffN examples. This allows us to study in detail the effects of finite training set size ff on, for example, the optimal choice of learning rate j. We also compare online and offline learning, for... (Update)

Context of citations to this paper:   More

.... [5, 6, 7] or di#erent kinds of approximations, such as Fokker Planck approaches [8, 9, 10, 11] and Gaussian local field distributions [12]. Exact work on non linear learning rules has drawn heavily on techniques from the spin glass and disordered systems community (for an...

.... [5, 6, 7] or different kinds of approximations, such as Fokker Planck approaches [8, 9, 10, 11] and Gaussian local field distributions [12]. Exact work on non linear learning rules has drawn heavily on techniques from the spin glass and disordered systems community (for an...

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BibTeX entry:   (Update)

D. Barber and P. Sollich. Online learning from finite training sets. In D. Saad, editor, On-Line Learning in Neural Networks, pages 279--302. Cambridge U.P., 1998. http://citeseer.ist.psu.edu/article/barber98online.html   More

@misc{ barber98online,
  author = "D. Barber and P. Sollich",
  title = "Online learning from finite training sets",
  text = "D. Barber and P. Sollich. Online learning from finite training sets. In
    D. Saad, editor, On-Line Learning in Neural Networks, pages 279--302. Cambridge
    U.P., 1998.",
  year = "1998",
  url = "citeseer.ist.psu.edu/article/barber98online.html" }
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1   Modern Analytic Techniques to Solve the Dynamics of Recurren.. (context) - Physics, -- et al. - 1996
1   line learning from finite training sets (context) - Physics, -- et al.
1   Online learning from finite training sets: An analytical cas.. (context) - Letters, Barber et al.
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