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Learning with Queries inside the Class of Unate k-quasi-Horn Formulas

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Abstract

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study restrictions of the class of unate k-quasi-Horn formulas. This class is known to be learnable with equivalence and membership queries. We prove that any subclass of unate k-quasi-Horn formulas can be properly learned with both types of queries, provided the set of consequents that appear in formulas can be computed efficiently in advance.

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References

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Authors and Affiliations

  1. Dpto. Sistemas Informáticos y Computación, Universidad Complutense, E-28040, Madrid, Spain

    Víctor Lavín Puente

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  1. Víctor Lavín Puente
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Correspondence to Víctor Lavín Puente.

Additional information

This work has been partially supported by the Spanish projects TIN2008-06622-C03-01, S2009TIC-1465 and UCM-BSCH-GR58/08-910502.

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Lavín Puente, V. Learning with Queries inside the Class of Unate k-quasi-Horn Formulas. New Gener. Comput. 33, 137–147 (2015). https://doi.org/10.1007/s00354-015-0204-8

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  • DOI: https://doi.org/10.1007/s00354-015-0204-8

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