The one-inclusion graph algorithm is near-optimal for the prediction model of learning

  • Authors:

  • Yi Li;P. M. Long;A. Srinivasan

  • Affiliations:

  • Dept. of Comput. Sci., Nat. Univ. of Singapore;-;-

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2006

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Abstract

Haussler, Littlestone and Warmuth (1994) described a general-purpose algorithm for learning according to the prediction model, and proved an upper bound on the probability that their algorithm makes a mistake in terms of the number of examples seen and the Vapnik-Chervonenkis (VC) dimension of the concept class being learned. We show that their bound is within a factor of 1+o(1) of the best possible such bound for any algorithm