Tracking drifting concepts using random examples
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Learning time-varying concepts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Tracking linear-threshold concepts with Winnow
The Journal of Machine Learning Research
Distributed training strategies for the structured perceptron
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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We generalize on-line learning to handle delays in receiving labels for instances. After receiving an instance x, the algorithm may need to make predictions on several new instances before the label for x is returned by the environment. We give two simple techniques for converting a traditional on-line algorithm into an algorithm for solving a delayed on-line problem. One technique is for instances generated by an adversary; the other is for instances generated by a distribution. We show how these techniques effect the original on-line mistake bounds by giving upper-bounds and restricted lower-bounds on the number of mistakes.