TY - GEN
T1 - Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics
AU - Chattopadhyay, Ishanu
AU - Wen, Yicheng
AU - Ray, Asok
AU - Phoha, Shashi
PY - 2011
Y1 - 2011
N2 - This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.
AB - This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.
UR - https://www.scopus.com/pages/publications/80053151144
UR - https://www.scopus.com/pages/publications/80053151144#tab=citedBy
U2 - 10.1109/acc.2011.5991453
DO - 10.1109/acc.2011.5991453
M3 - Conference contribution
AN - SCOPUS:80053151144
SN - 9781457700804
T3 - Proceedings of the American Control Conference
SP - 125
EP - 130
BT - Proceedings of the 2011 American Control Conference, ACC 2011
ER -