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    Prediction with expert evaluators’ advice

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    Authors
    Chernov, Alexey
    Vovk, Vladimir
    Issue Date
    2009
    
    Metadata
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    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” experts. In this case, the defensive forecasting algorithm reduces to a simple modification of the Aggregating Algorithm.
    Citation
    Chernov. A. and Vovk, V., (2009) 'Prediction with Expert Evaluators’ Advice' in Algorithmic Learning Theory, proceedings of the 20th International Conference, ALT 2009, vol. 5809: 8-22
    Publisher
    Springer
    Journal
    Algorithmic Learning Theory
    URI
    http://hdl.handle.net/10547/279180
    DOI
    10.1007/978-3-642-04414-4_6
    Additional Links
    http://link.springer.com/chapter/10.1007%2F978-3-642-04414-4_6?LI=true
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783642044137
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-04414-4_6
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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