Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Artificial Intelligence - Special issue on relevance
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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There are two main families of on-line algorithms depending on whether a relative entropy or a squared Euclidean distance is used as a regularizer. The difference between the two families can be dramatic. The question is whether one can always achieve comparable performance by replacing the relative entropy regularization by the squared Euclidean distance plus additional linear constraints. We formulate a simple open problem along these lines for the case of learning disjunctions.