The problem of the efficient computation of the relative entropy of two distributions represented by deterministic weighted automata arises in several machine learning problems. We show that this problem can be naturally formulated as a shortest-distance problem over an intersection automaton denned on an appropriate semiring. We describe simple and efficient novel algorithms for its computation and report the results of experiments demonstrating the practicality of our algorithms for very large weighted automata. Our algorithms apply to unambiguous weighted automata, a class of weighted automata that strictly includes deterministic weighted automata. These are also the first algorithms extending the computation of entropy or of relative entropy beyond the class of deterministic weighted automata. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Cortes, C., Mohri, M., Rastogi, A., & Riley, M. D. (2006). Efficient computation of the relative entropy of probabilistic automata. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3887 LNCS, pp. 323–336). https://doi.org/10.1007/11682462_32
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