Abstract
An algorithm is presented for online prediction that allows to track the best expert efficiently even if the number of experts is exponentially large, provided that the set of experts has a certain structure allowing efficient implementations of the exponentially weighted average predictor. As an example we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over the edges in the path.
This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada, the NATO Science Fellowship of Canada, the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, Spanish Ministry of Science and Technology and FEDER, grant BMF2003-03324, and by the PASCAL Network of Excellence under EC grant no. 506778.
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György, A., Linder, T., Lugosi, G. (2005). Tracking the Best of Many Experts. In: Auer, P., Meir, R. (eds) Learning Theory. COLT 2005. Lecture Notes in Computer Science(), vol 3559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11503415_14
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DOI: https://doi.org/10.1007/11503415_14
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