Abstract
Herein we investigate learning in the limit where confidence in the current conjecture accrues with time. Confidence levels are given by rational numbers between 0 and 1. The traditional requirement that for learning in the limit is that a device must converge (in the limit) to a correct answer. We further demand that the associated confidence in the answer (monotonically) approach 1 in the limit. In addition to being a more realistic model of learning, our new notion turns out to be a more powerful as well. In addition, we give precise characterizations of the classes of functions that are learnable in our new model(s).
This project was supported by National Science Foundation Cooperative Agreement Grant CCR 9421640.
The second author was supported by Latvian Science Council Grant No.93.599.
The third author was supported in part by NSF Grant 9301339.
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Bārzdiņs, J., Freivalds, R., Smith, C.H. (1996). Learning with confidence. In: Puech, C., Reischuk, R. (eds) STACS 96. STACS 1996. Lecture Notes in Computer Science, vol 1046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60922-9_18
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DOI: https://doi.org/10.1007/3-540-60922-9_18
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