TY - GEN
T1 - Adaptation in symbolic dynamic systems for pattern classification
AU - Wen, Yicheng
AU - Mukherjee, Kushal
AU - Ray, Asok
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84869407126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869407126&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84869407126
SN - 9781457710957
T3 - Proceedings of the American Control Conference
SP - 697
EP - 702
BT - 2012 American Control Conference, ACC 2012
T2 - 2012 American Control Conference, ACC 2012
Y2 - 27 June 2012 through 29 June 2012
ER -