Prediction with expert evaluators' advice Export

@article{citeulike:4212542, abstract = {We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner's and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner's goal is to perform better or not much worse than each expert, as evaluated by that expert, for all experts simultaneously. If the loss functions used by the experts are all proper scoring rules and all mixable, we show that the defensive forecasting algorithm enjoys the same performance guarantee as that attainable by the Aggregating Algorithm in the standard setting and known to be optimal. This result is also applied to the case of "specialist" (or "sleeping") experts. In this case, the defensive forecasting algorithm reduces to a simple modification of the Aggregating Algorithm.}, archivePrefix = {arXiv}, author = {Chernov, Alexey and Vovk, Vladimir}, citeulike-article-id = {4212542}, citeulike-linkout-0 = {http://arxiv.org/abs/0902.4127}, citeulike-linkout-1 = {http://arxiv.org/pdf/0902.4127}, comment = {John Langford mentions this paper appearing at ALT 2009: http://hunch.net/?p=992}, day = {23}, eprint = {0902.4127}, keywords = {algorithm, experts, loss, online, protocol}, month = {Mar}, posted-at = {2009-03-24 03:42:28}, priority = {5}, title = {Prediction with expert evaluators' advice}, url = {http://arxiv.org/abs/0902.4127}, year = {2009} }

TY - JOUR ID - citeulike:4212542 L3 - citeulike-article-id:4212542 N2 - We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner's and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner's goal is to perform better or not much worse than each expert, as evaluated by that expert, for all experts simultaneously. If the loss functions used by the experts are all proper scoring rules and all mixable, we show that the defensive forecasting algorithm enjoys the same performance guarantee as that attainable by the Aggregating Algorithm in the standard setting and known to be optimal. This result is also applied to the case of "specialist" (or "sleeping") experts. In this case, the defensive forecasting algorithm reduces to a simple modification of the Aggregating Algorithm. TI - Prediction with expert evaluators' advice KW - algorithm KW - experts KW - loss KW - online KW - protocol N1 - John Langford mentions this paper appearing at ALT 2009: http://hunch.net/?p=992 AU - Chernov, Alexey AU - Vovk, Vladimir PY - 2009/Mar/23/ UR - http://arxiv.org/abs/0902.4127 ER -