COLT '90 Proceedings of the third annual workshop on Computational learning theory
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Beating the hold-out: bounds for K-fold and progressive cross-validation
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Online Learning and Online Convex Optimization
Foundations and Trends® in Machine Learning
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We investigate the generalization behavior of sequential prediction (online) algorithms, when data are generated from a probability distribution. Using some newly developed probability inequalities, we are able to bound the total generalization performance of a learning algorithm in terms of its observed total loss. Consequences of this analysis will be illustrated with examples.