Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

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Background: The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by Mikl

Alexander G. Churbanov, Stephen Winters-Hilt

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BMCBI 2008 | Linear Memory | Linear Memory Implementation | Sequence Length |

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Added	09 Dec 2010
Updated	09 Dec 2010
Type	Journal
Year	2008
Where	BMCBI
Authors	Alexander G. Churbanov, Stephen Winters-Hilt

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Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

BMCBI 2008 | Linear Memory | Linear Memory Implementation | Sequence Length |

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