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The Journal of Artificial Intelligence Research
Article . 2006 . Peer-reviewed
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The Journal of Artificial Intelligence Research
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Preprint . 2011
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Logical Hidden Markov Models

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 19 Apr 2006Embargo end date: 01 Jan 2011 Belgium Publisher:AI Access FoundationJournal:Journal of Artificial Intelligence Research, volume 25, pages 425-456 (eissn: 1076-9757, Copyright policy )
Authors: Kersting, K.; De Raedt, Luc; Raiko, T.;

doi: 10.1613/jair.1675 , 10.48550/arxiv.1109.2148

arXiv: http://arxiv.org/abs/1109.2148

Logical Hidden Markov Models

Abstract

Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.

Country
Belgium
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Keywords

FOS: Computer and information sciences, knowledge, Technology, Science & Technology, 4602 Artificial intelligence, Computer Science - Artificial Intelligence, 1702 Cognitive Sciences, cs.AI, Computer Science, Artificial Intelligence, 4603 Computer vision and multimedia computation, Artificial Intelligence (cs.AI), 4611 Machine learning, sequence-analysis, 0102 Applied Mathematics, Computer Science, 0801 Artificial Intelligence and Image Processing, context-free grammars, KNOWLEDGE, Artificial Intelligence & Image Processing, CONTEXT-FREE GRAMMARS

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 54
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
54
Top 10%
Top 10%
Top 10%
Green
gold
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