Title :
The one-inclusion graph algorithm is near-optimal for the prediction model of learning
Author :
Li, Yi ; Long, Philip M. ; Srinivasan, Aravind
Author_Institution :
Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore
fDate :
3/1/2001 12:00:00 AM
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 :
graph theory; learning systems; optimisation; prediction theory; probability; Vapnik-Chervonenkis dimension; concept class; general-purpose algorithm; learning prediction model; near-optimal algorithm; one-inclusion graph algorithm; probability; upper bound; Computer science; Neural networks; Prediction algorithms; Predictive models; Probability distribution; Upper bound; Virtual colonoscopy;
Journal_Title :
Information Theory, IEEE Transactions on
