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Unsupervised inductive learning in symbolic sequences via Recursive Identification of Self-Similar Semantics | IEEE Conference Publication | IEEE Xplore

Unsupervised inductive learning in symbolic sequences via Recursive Identification of Self-Similar Semantics


Abstract:

This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as...Show More

Abstract:

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.
Date of Conference: 29 June 2011 - 01 July 2011
Date Added to IEEE Xplore: 18 August 2011
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Conference Location: San Francisco, CA, USA

References

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