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CAI
2015
Springer

Learning Weighted Automata

7 years 11 months ago
Learning Weighted Automata
Many tasks in text and speech processing and computational biology require estimating functions mapping strings to real numbers. A broad class of such functions can be defined by weighted automata. Spectral methods based on the singular value decomposition of a Hankel matrix have been recently proposed for learning a probability distribution represented by a weighted automaton from a training sample drawn according to this same target distribution. In this paper, we show how spectral methods can be extended to the problem of learning a general weighted automaton from a sample generated by an arbitrary distribution. The main obstruction to this approach is that, in general, some entries of the Hankel matrix may be missing. We present a solution to this problem based on solving a constrained matrix completion problem. Combining these two ingredients, matrix completion and spectral method, a whole new family of algorithms for learning general weighted automata is obtained. We present ge...
Borja Balle, Mehryar Mohri
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where CAI
Authors Borja Balle, Mehryar Mohri
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