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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 Miklós, I. and Meyer, I.M. describes a memory sparse version of th...
| Autores principales: | , |
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| Formato: | Texto |
| Lenguaje: | English |
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BioMed Central
2008
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2430973/ https://www.ncbi.nlm.nih.gov/pubmed/18447951 http://dx.doi.org/10.1186/1471-2105-9-224 |