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Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower Bounds for Hard Sets

SIAM J. Comput. Volume 36, Issue 6, pp. 1696-1708 (2007)

Published March 19, 2007
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We establish a relationship between the online mistake-bound model of learning and resource-bounded dimension. This connection is combined with the Winnow algorithm to obtain new results about the density of hard sets under adaptive reductions. This improves previous work of Fu [SIAM J. Comput., 24 (1995), pp. 1082–1090] and Lutz and Zhao [SIAM J. Comput., 30 (2000), pp. 1197–1210], and solves one of Lutz and Mayordomo's “twelve problems in resource-bounded measure” [Bull. Eur. Assoc. Theor. Comput. Sci. EATSC, 68 (1999), pp. 64–80].

©2007 Society for Industrial and Applied Mathematics
History: Received December 13, 2005; accepted September 20, 2006; published March 19, 2007
Permalink: http://dx.doi.org/10.1137/050647517

PUBLICATION DATA

ISSN:
0097-5397 (print)   1095-7111 (online)
Publisher:
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