Continuous Experts and the Binning Algorithm

We consider the design of online master algorithms for combining the predictions from a set of experts where the absolute loss of the master is to be close to the absolute loss of the best expert. For the case when the master must produce binary predictions, the Binomial Weighting algorithm is known...

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Bibliographic Details
Published inLearning Theory pp. 544 - 558
Main Authors Abernethy, Jacob, Langford, John, Warmuth, Manfred K.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Absolute Loss
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Continuous Quantity
Exact sciences and technology
Exponential Weight
Good Expert
Successor State
On line
Weighting
Optimal algorithm
Artificial intelligence
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Summary:We consider the design of online master algorithms for combining the predictions from a set of experts where the absolute loss of the master is to be close to the absolute loss of the best expert. For the case when the master must produce binary predictions, the Binomial Weighting algorithm is known to be optimal when the number of experts is large. It has remained an open problem how to design master algorithms based on binomial weights when the predictions of the master are allowed to be real valued. In this paper we provide such an algorithm and call it the Binning algorithm because it maintains experts in an array of bins. We show that this algorithm is optimal in a relaxed setting in which we consider experts as continuous quantities. The algorithm is efficient and near-optimal in the standard experts setting.
ISBN:3540352945
9783540352945
ISSN:0302-9743
1611-3349
DOI:10.1007/11776420_40