Bridging Viterbi and Posterior Decoding : A Generalized Risk Approach to Hidden Path Inference Based on Hidden Markov Models. / Lember, Jüri; Koloydenko, Alexey A.

In: Journal of Machine Learning Research, Vol. 15, 01.2014, p. 1-58.

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Abstract

Motivated by the unceasing interest in hidden Markov models (HMMs), this paper re-examines hidden path inference in these models, using primarily a risk-based framework. While the most common maximum a posteriori (MAP), or Viterbi, path estimator and the minimum error, or Posterior Decoder (PD) have long been around, other path estimators, or decoders, have been either only hinted at or applied more recently and in dedicated applications generally unfamiliar to the statistical learning community. Over a decade ago, however, a family of algorithmically dened decoders aiming to hybridize the two
standard ones was proposed elsewhere. The present paper gives a careful analysis of this hybridization approach, identies several problems and issues with it and other previously proposed approaches, and proposes practical resolutions of those. Furthermore, simple modications of the classical criteria for hidden path recognition are shown to lead to a new class of decoders. Dynamic programming algorithms to compute these decoders in the usual forward-backward manner are presented. A particularly interesting subclass of
such estimators can be also viewed as hybrids of the MAP and PD estimators. Similar to previously proposed MAP-PD hybrids, the new class is parameterized by a small number of tunable parameters. Unlike their algorithmic predecessors, the new risk-based decoders are more clearly interpretable, and, most importantly, work \out-of-the box" in practice, which is demonstrated on some real bioinformatics tasks and data. Some further generalizations
and applications are discussed in the conclusion.
Original languageEnglish
Pages (from-to)1-58
Number of pages58
JournalJournal of Machine Learning Research
Volume15
Publication statusPublished - Jan 2014
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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