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

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

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Alexander G. Churbanov, Stephen Winters-Hilt
• Keywords: BMCBI 2008, Linear Memory, Linear Memory Implementation, Sequence Length