Criterion of calibration for transductive confidence machine with limited feedback

Authors:
Ilia Nouretdinov;Vladimir Vovk
Affiliations:
Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK;Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK
Venue:
Theoretical Computer Science - Algorithmic learning theory
Year:
2006

Citing 4
Cited 0

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Abstract

This paper is concerned with the problem of on-line prediction in the situation where some data are unlabelled and can never be used for prediction, and even when the data are labelled, the labels may arrive with a delay. We construct a modification of randomised transductive confidence machine for this case and prove a necessary and sufficient condition for its predictions being calibrated, in the sense that in the long run they are wrong with a prespecified probability under the assumption that the data are generated independently by the same distribution. The condition for calibration turns out to be very weak: feedback should be given on more than a logarithmic fraction of steps.