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International Conference on Implementation and Application of Automata

CIAA 2018: Implementation and Application of Automata pp 146–158Cite as

Algorithms and Training for Weighted Multiset Automata and Regular Expressions

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10977))

Abstract

Multiset automata are a class of automata for which the symbols can be read in any order and obtain the same result. We investigate weighted multiset automata and show how to construct them from weighted regular expressions. We present training methods to learn the weights for weighted regular expressions and for general multiset automata from data. Finally, we examine situations in which inside weights can be computed more efficiently.

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Acknowledgements

We would like to thank the anonymous reviewers for their very detailed and helpful comments.

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via AFRL Contract #FA8650-17-C-9116. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA

    Justin DeBenedetto & David Chiang

Authors
  1. Justin DeBenedetto
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  2. David Chiang
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Correspondence to Justin DeBenedetto .

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Editors and Affiliations

  1. University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada

    Cezar Câmpeanu

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DeBenedetto, J., Chiang, D. (2018). Algorithms and Training for Weighted Multiset Automata and Regular Expressions. In: Câmpeanu, C. (eds) Implementation and Application of Automata. CIAA 2018. Lecture Notes in Computer Science(), vol 10977. Springer, Cham. https://doi.org/10.1007/978-3-319-94812-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-94812-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94811-9

  • Online ISBN: 978-3-319-94812-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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