A hybrid self-organizing model for sequence analysis

Ferles, C; Stafylopatis, A

dc.contributor.author	Ferles, C	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:45:02Z
dc.date.available	2014-03-01T02:45:02Z
dc.date.issued	2008	en
dc.identifier.issn	10823409	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32108
dc.subject	Cross Section	en
dc.subject	Dimensional Reduction	en
dc.subject	Dynamic Programming Algorithm	en
dc.subject	Large Scale	en
dc.subject	Learning Algorithm	en
dc.subject	Self Organization	en
dc.subject	Self Organized Map	en
dc.subject	Sequence Analysis	en
dc.subject	Sequence Space	en
dc.subject	Theoretical Foundation	en
dc.subject	Gradient Descent	en
dc.subject	Hidden Markov Model	en
dc.subject.other	Algorithmic realizations	en
dc.subject.other	Dimensionality reductions	en
dc.subject.other	Dynamic Programming algorithms	en
dc.subject.other	Fully integrated	en
dc.subject.other	Gradient descent learning algorithms	en
dc.subject.other	Self-organizing	en
dc.subject.other	Sequence analysis	en
dc.subject.other	Sequence spaces	en
dc.subject.other	Unsupervised trainings	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Conformal mapping	en
dc.subject.other	FORTH (programming language)	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Learning systems	en
dc.subject.other	Markov processes	en
dc.subject.other	Object recognition	en
dc.subject.other	Strength of materials	en
dc.subject.other	Systems engineering	en
dc.subject.other	Learning algorithms	en
dc.title	A hybrid self-organizing model for sequence analysis	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICTAI.2008.108	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICTAI.2008.108	en
heal.identifier.secondary	4669762	en
heal.publicationDate	2008	en
heal.abstract	The Self-Organizing Hidden Markov Model Map (SOHMMM) constitutes a cross-section between the theoretic foundations and algorithmic realizations of the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM). The intimate fusion and synergy of the SOM unsupervised training and HMM dynamic programming algorithms brings forth a novel on-line gradient descent learning algorithm, which is fully integrated into the SOHMMM. The model is presented from both a theoretical and algorithmic perspective. The SOHMMM can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. © 2008 IEEE.	en
heal.journalName	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI	en
dc.identifier.doi	10.1109/ICTAI.2008.108	en
dc.identifier.volume	2	en
dc.identifier.spage	105	en
dc.identifier.epage	112	en