(Enter summary)
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
9: Learning quickly when irrelevant attributes abound: A new linearthreshold algori.. (context) - Littlestone - 1988
8: Relative loss bounds for multidimensional regression problems
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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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