Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8585
DC FieldValueLanguage
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.authorDemiris, Yiannis-
dc.date.accessioned2016-07-01T11:39:14Z-
dc.date.available2016-07-01T11:39:14Z-
dc.date.issued2013-10-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, vol. 35, no. 6, pp. 1523 -1534en_US
dc.identifier.issn01628828-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8585-
dc.description.abstractSequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF ($({rm CRF}^{infty })$) model is experimentally demonstrated.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.rights© IEEEen_US
dc.subjectConditional random fielden_US
dc.subjectMean-field principleen_US
dc.subjectSequence memoizeren_US
dc.subjectSequential dataen_US
dc.titleThe Infinite-Order Conditional Random Field Model for Sequential Data Modelingen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationImperial College Londonen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/TPAMI.2012.208en_US
dc.dept.handle123456789/134en
dc.relation.issue6en_US
dc.relation.volume35en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage1523en_US
dc.identifier.epage1534en_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.journal.journalissn1939-3539-
crisitem.journal.publisherIEEE-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
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