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Linear Hinge Loss and Average Margin (1998)  (Make Corrections)  (14 citations)
Claudio Gentile, Manfred K. Warmuth



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Abstract: We describe a unifying method for proving relative loss bounds for online linear threshold classification algorithms, such as the Perceptron and the Winnow algorithms. For classification problems the discrete loss is used, i.e., the total number of prediction mistakes. We introduce a continuous loss function, called the "linear hinge loss", that can be employed to derive the updates of the algorithms. We first prove bounds w.r.t. the linear hinge loss and then convert them to the discrete ... (Update)

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9:   General convergence results for linear discriminant updates - Grove, Littlestone et al. - 1997
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BibTeX entry:   (Update)

C. Gentile and M. K. Warmuth. Linear hinge loss and average margin. In Proc. NIPS'98, 1998. http://citeseer.ist.psu.edu/gentile98linear.html   More

@misc{ gentile98linear,
  author = "C. Gentile and M. Warmuth",
  title = "Linear hinge loss and average margin",
  text = "C. Gentile and M. K. Warmuth. Linear hinge loss and average margin. In
    Proc. NIPS'98, 1998.",
  year = "1998",
  url = "citeseer.ist.psu.edu/gentile98linear.html" }
Citations (may not include all citations):
84   Learning when irrelevant attributes abound: A new linearthre.. (context) - Littlestone - 1988
74   Mistake Bounds and Logarithmic Linear-threshold Learning Alg.. (context) - Littlestone - 1989
73   Additive versus exponentiated gradient updates for linear pr.. (context) - Kivinen, Warmuth - 1997
64   and linear threshold learning using Winnow (context) - Littlestone, attributes et al. - 1991
50   Large margin classification using the perceptron algorithm - Freund, Schapire - 1998
49   General convergence results for linear discriminant updates - Grove, Littlestone et al. - 1997
35   The relaxation method of finding the common point of convex .. (context) - Bregman - 1967
30   Relative loss bounds for multidimensional regression problem.. - Kivinen, Warmuth - 1998
27   logarithmic mistake bounds when few input variables are rele.. (context) - Kivinen, Warmuth et al. - 1997
22   Worst-case loss bounds for sigmoided linear neurons (context) - Helmbold, Kivinen et al.
4   Relative loss bounds and the exponential family of distribut.. (context) - Azoury, Warmuth - 1998



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