@article {17667,
	title = {The one-inclusion graph algorithm is near-optimal for the prediction model of learning},
	journal = {IEEE Transactions on Information Theory},
	volume = {47},
	year = {2001},
	month = {2001/03//},
	pages = {1257 - 1261},
	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},
	keywords = {Computer science, concept class, general-purpose algorithm, graph theory, learning prediction model, learning systems, near-optimal algorithm, Neural networks, one-inclusion graph algorithm, optimisation, Prediction algorithms, prediction theory, Predictive models, probability, Probability distribution, Upper bound, Vapnik-Chervonenkis dimension, Virtual colonoscopy},
	isbn = {0018-9448},
	doi = {10.1109/18.915700},
	author = {Li,Yi and Long,P. M and Srinivasan, Aravind}
}
