Optimal Alignment Algorithm for Context-Sensitive Hidden Markov Models Conference Paper

abstract

• The hidden Markov model is well-known for its efficiency in modeling short-term dependencies between adjacent samples. However, it cannot be used for modeling longer-range interactions between symbols that are distant from each other. In this paper, we introduce the concept of context-sensitive HMM that is capable of modeling strong pairwise correlations between distant symbols. Based on this model, we propose a polynomial-time algorithm that can be used for finding the optimal state sequence of an observed symbol string. The proposed model is especially useful in modeling palindromes, which has an important application in RNA secondary structure analysis. 2005 IEEE.

name of conference

• Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

authors

• Yoon, Byung-Jun

published proceedings

• Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

author list (cited authors)

• Yoon, B., & Vaidyanathan, P. P.

citation count

• 5

complete list of authors

• Yoon, Byung-Jun||Vaidyanathan, PP

publication date

• January 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

keywords

• Networking And Information Technology R&d

Digital Object Identifier (DOI)

• 10.1109/icassp.2005.1416003

International Standard Book Number (ISBN) 10

• 0780388747

International Standard Book Number (ISBN) 13

• 9780780388741

start page

• iv-293-iv-296

volume

• 4

URL

• http://dx.doi.org/10.1109/icassp.2005.1416003