Structured prediction has become very important in recent years. A simple but notable class of structured prediction is one for sequences, so-called sequential labeling. For sequential labeling, it is often required to take a summation over all the possible output sequences, when estimating the parameters of a probabilistic model for instance. We cannot make the direct calculation of such a summation from its definition in practice. Although the ordinary forward-backward algorithm provides an efficient way to do it, it is applicable to limited types of summations. In this paper, we propose a generalization of the forward-backward algorithm, by which we can calculate much broader types of summations than the existing forward-backward algorithms. We show that this generalization subsumes some existing calculations required in past studies, and we also discuss further possibilities of this generalization. © 2009 Springer.
CITATION STYLE
Azuma, A., & Matsumoto, Y. (2009). A generalization of forward-backward algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5781 LNAI, pp. 99–114). https://doi.org/10.1007/978-3-642-04180-8_24
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