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Abstract: The problem of predicting the next outcome of an individual binary sequence, based on noisy observations of the past, is considered. The goal of the predictor is to perform, for each individual sequence, "almost" as well as the best in a set of experts, where performance is evaluated using a general loss function. A comprehensive approach to prediction in this noisy setting is presented and proven generally efficient under appropriate conditions. As an illustration of the applicability of... (Update)
Context of citations to this paper: More
...method to de ne randomized predictors for binary ergodic processes. Related methods were recently applied by Weissman and Merhav [42, 43] to the prediction of individual and ergodic binary sequences. Aggregating methods were applied in a di erent way by Foster [18] to...
...code. This is done by employing the horizon dependent source code of Theorem 1 on blocks of exponentially increasing lengths (cf. e.g. [1, 15] for typical examples of such constructions) Proof: Fix an l N and divide the time axis, i = 1; 2; N , into n = N=l...
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BibTeX entry: (Update)
T. Weissman and N. Merhav, "Universal Prediction of Individual Binary Sequences in the Presence of Arbitrarily Varying, Memoryless Additive Noise," accepted to ISIT 2000. http://citeseer.ist.psu.edu/weissman99universal.html More
@article{ weissman01universal,
author = "Weissman and Merhav",
title = "Universal Prediction of Individual Binary Sequences in the Presence of Noise",
journal = "IEEETIT: IEEE Transactions on Information Theory",
volume = "47",
year = "2001",
url = "citeseer.ist.psu.edu/weissman99universal.html" }
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