Abstract
This paper is concerned with the problem of on-line prediction in the situation where some data is unlabelled and can never be used for prediction, and even when data is 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 data is generated independently by same distribution. The condition for calibration turns out to be very weak: feedback should be given on more than a logarithmic fraction of steps.
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References
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© 2003 Springer-Verlag Berlin Heidelberg
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Nouretdinov, I., Vovk, V. (2003). Criterion of Calibration for Transductive Confidence Machine with Limited Feedback. In: Gavaldá, R., Jantke, K.P., Takimoto, E. (eds) Algorithmic Learning Theory. ALT 2003. Lecture Notes in Computer Science(), vol 2842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39624-6_21
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DOI: https://doi.org/10.1007/978-3-540-39624-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20291-2
Online ISBN: 978-3-540-39624-6
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