Abstract
We show that multiplicity automata (MAs) with size n and input alphabet ∑ can efficiently be learned from n(n+1)| ∑ |+2 smallest counterexamples. This improves on an earlier result of Bergadano and Varricchio. A unique representation for MAs is introduced. Our algorithm learns this representation. We also show that any learning algorithm for MAs needs at least \( \frac{1} {{64}}n^2 |\Sigma | \) smallest counterexamples. Thus our upper bound on the number of counterexamples cannot be improved substantially.
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© 1999 Springer-Verlag Berlin Heidelberg
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Forster, J. (1999). Learning Multiplicity Automata from Smallest Counterexamples. In: Fischer, P., Simon, H.U. (eds) Computational Learning Theory. EuroCOLT 1999. Lecture Notes in Computer Science(), vol 1572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49097-3_7
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DOI: https://doi.org/10.1007/3-540-49097-3_7
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