A linear memory algorithm for Baum-Welch training |
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Authors: | István?Miklós Email author" target="_blank">Irmtraud?M?MeyerEmail author |
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Institution: | 1.MTA-ELTE Theoretical Biology and Ecology Group,Pázmány Péter sétány 1/c,Budapest,Hungary;2.European Bioinformatics Institute,Wellcome Trust Genome Campus,Cambridge,UK |
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Abstract: | Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden
Markov models in a fully automated way. It can be employed as long as a training set of annotated sequences is known, and
provides a rigorous way to derive parameter values which are guaranteed to be at least locally optimal. For complex hidden
Markov models such as pair hidden Markov models and very long training sequences, even the most efficient algorithms for Baum-Welch
training are currently too memory-consuming. This has so far effectively prevented the automatic parameter training of hidden
Markov models that are currently used for biological sequence analyses. |
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