Adaptation in symbolic dynamic systems for pattern classification

Yicheng Wen, Kushal Mukherjee, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the problem of pattern classification in the symbolic dynamic domain, where the patterns of interest are represented by probabilistic finite state automata (PFSA) with possibly dissimilar algebraic structures. A combination of Dirichlet and multinomial distributions is used to model the uncertainties due to the finite length approximation of symbol strings. The classifier algorithm follows the structure of a Bayes model and has been validated on a simulation test bed.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
Pages697-702
Number of pages6
StatePublished - Nov 26 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2012 American Control Conference, ACC 2012
CountryCanada
CityMontreal, QC
Period6/27/126/29/12

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Wen, Y., Mukherjee, K., & Ray, A. (2012). Adaptation in symbolic dynamic systems for pattern classification. In 2012 American Control Conference, ACC 2012 (pp. 697-702). [6314678] (Proceedings of the American Control Conference).