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Learning Weighted Automata

  • Borja Balle
  • Mehryar MohriEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9270)

Abstract

Weighted finite automata (WFA) are finite automata whose transitions and states are augmented with some weights, elements of a semiring. A WFA induces a function over strings. The value it assigns to an input string is the semiring sum of the weights of all paths labeled with that string, where the weight of a path is obtained by taking the semiring product of the weights of its constituent transitions, as well as those of its origin and destination states.

Keywords

Hide Markov Model Singular Value Decomposition Transition Weight Hankel Matrix Nuclear Norm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer ScienceMcGill UniversityMontréalCanada
  2. 2.Courant Institute of Mathematical SciencesNew YorkUSA
  3. 3.Google ResearchNew YorkUSA

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