首页|Optimal probability aggregation based on generalized brier scoring
Optimal probability aggregation based on generalized brier scoring
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NETL
NSTL
Springer Nature
In this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterization results for linear aggregation exist. However, in linear aggregation weights are not fixed, but free parameters. We show how fixing such weights by success-based scores, a generalization of Brier scoring, allows for transferring the mentioned optimality results to the case of probability aggregation.
Probability aggregationBrier scoreMeta-inductionMachine learningNo regret algorithms
Feldbacher-Escamilla, Christian J.、Schurz, Gerhard
NETL
NSTL
Springer Nature
