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https://hdl.handle.net/20.500.14279/1651
Τίτλος: | The Echo State Conditional Random Field Model for Sequential Data Modeling | Συγγραφείς: | Chatzis, Sotirios P. Demiris, Yiannis |
metadata.dc.contributor.other: | Χατζής, Σωτήριος Δεμίρης, Γιάννης |
Major Field of Science: | Engineering and Technology | Field Category: | Computer and Information Sciences | Λέξεις-κλειδιά: | Computer science;Artificial intelligence;Expert systems (Computer science);Computational linguistics;Regression analysis;Echo-state networks;Sequence segmentation;Conditional random fields;Signal labeling | Ημερομηνία Έκδοσης: | 1-Σεπ-2012 | Πηγή: | Expert systems with applications, 2012, vol. 39, no. 11, pp. 10303–10309 | Volume: | 39 | Issue: | 11 | Start page: | 10303 | End page: | 10309 | Περιοδικό: | Expert systems with applications | Περίληψη: | 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 | URI: | https://hdl.handle.net/20.500.14279/1651 | ISSN: | 09574174 | DOI: | 10.1016/j.eswa.2012.02.193 | Rights: | © 2012 Elsevier | Type: | Article | Affiliation: | Imperial College London | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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