Higher-order hidden markov models with applications to DNA sequences

Wai Ki Ching*, Eric S. Fung, Michael K. Ng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

18 Citations (Scopus)

Abstract

Hidden Markov models (HMMs) have been applied to many real-world applications. Very often HMMs only deal with the first order transition probability distribution among the hidden states. In this paper we develop higher-order HMMs. We study the evaluation of the probability of a sequence of observations based on higher-order HMMs and determination of a best sequence of model states.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning
Subtitle of host publication4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers
EditorsJiming Liu, Yiu-ming Cheung, Hujun Yin
PublisherSpringer Berlin Heidelberg
Pages535-539
Number of pages5
Edition1st
ISBN (Electronic)9783540450801
ISBN (Print)9783540405504
DOIs
Publication statusPublished - 29 Jul 2003
Event4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003 - Hong Kong Convention and Exhibition Centre, Hong Kong, China
Duration: 21 Mar 200323 Mar 2003
https://link.springer.com/book/10.1007/b11717
http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference website)
http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference program)

Publication series

NameLecture Notes in Computer Science
Volume2690
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameIDEAL: International Conference on Intelligent Data Engineering and Automated Learning

Conference

Conference4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003
Country/TerritoryChina
CityHong Kong
Period21/03/0323/03/03
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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