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|Title:||The one-inclusion graph algorithm is near-optimal for the prediction model of learning||Authors:||Li, Y.
One-inclusion graph algorithm
Vapnik-Chervonenkis (VC) dimension
|Issue Date:||2001||Citation:||Li, Y., Long, P.M., Srinivasan, A. (2001). The one-inclusion graph algorithm is near-optimal for the prediction model of learning. IEEE Transactions on Information Theory 47 (3) : 1257-1261. ScholarBank@NUS Repository. https://doi.org/10.1109/18.915700||Abstract:||Haussler, Littlestone, and Warmuth 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.||Source Title:||IEEE Transactions on Information Theory||URI:||http://scholarbank.nus.edu.sg/handle/10635/43369||ISSN:||00189448||DOI:||10.1109/18.915700|
|Appears in Collections:||Staff Publications|
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