Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1651
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.contributor.author | Demiris, Yiannis | - |
dc.contributor.other | Χατζής, Σωτήριος | - |
dc.contributor.other | Δεμίρης, Γιάννης | - |
dc.date.accessioned | 2013-02-19T15:47:41Z | en |
dc.date.accessioned | 2013-05-17T05:22:05Z | - |
dc.date.accessioned | 2015-12-02T09:55:27Z | - |
dc.date.available | 2013-02-19T15:47:41Z | en |
dc.date.available | 2013-05-17T05:22:05Z | - |
dc.date.available | 2015-12-02T09:55:27Z | - |
dc.date.issued | 2012-09-01 | - |
dc.identifier.citation | Expert systems with applications, 2012, vol. 39, no. 11, pp. 10303–10309 | en_US |
dc.identifier.issn | 09574174 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1651 | - |
dc.description.abstract | Sequential 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 such 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 do not account for temporal dependencies between the observed variables – they only postulate Markovian interdependencies between the predicted label variables. To resolve these issues, in this paper we propose a non-linear hierarchical CRF formulation that combines the power of echo state networks to extract high level temporal features with the graphical framework of CRF models, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Expert systems with applications | en_US |
dc.rights | © 2012 Elsevier | en_US |
dc.subject | Computer science | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Expert systems (Computer science) | en_US |
dc.subject | Computational linguistics | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Echo-state networks | en_US |
dc.subject | Sequence segmentation | en_US |
dc.subject | Conditional random fields | en_US |
dc.subject | Signal labeling | en_US |
dc.title | The Echo State Conditional Random Field Model for Sequential Data Modeling | en_US |
dc.type | Article | en_US |
dc.collaboration | Imperial College London | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.eswa.2012.02.193 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 11 | en_US |
dc.relation.volume | 39 | en_US |
cut.common.academicyear | 2012-2013 | en_US |
dc.identifier.spage | 10303 | en_US |
dc.identifier.epage | 10309 | en_US |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
crisitem.journal.journalissn | 0957-4174 | - |
crisitem.journal.publisher | Elsevier | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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