Evolving the Structure of Hidden Markov Models
Won, Kyoung-Jae, Prügel-Bennett, Adam and Krogh, Anders (2006) Evolving the Structure of Hidden Markov Models. IEEE Transactions on Evolutionary Computation, 10, (1), 39-49.
A Genetic Algorithm (GA) is proposed for finding the structure of Hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimisation of the emission and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a hand-crafted model that has been published in the literature.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||08 May 2007|
|Last Modified:||07 Mar 2012 14:56|
|Contributors:||Won, Kyoung-Jae (Author)
Prügel-Bennett, Adam (Author)
Krogh, Anders (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||8|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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