Please use this identifier to cite or link to this item:
|Title:||The echo state conditional random field model for sequential data modeling||Authors:||Chatzis, Sotirios P.
Chatzis, Sotirios P.
Expert systems (Computer science)
|Issue Date:||2012||Publisher:||Elsevier||Source:||Expert systems with applications, 2012, Volume 39, Issue 11, Pages 10303–10309||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||URI:||http://ktisis.cut.ac.cy/handle/10488/7237||ISSN:||0957-4174||DOI:||10.1016/j.eswa.2012.02.193||Rights:||© 2012 Elsevier Ltd. All rights reserved|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Mar 31, 2017
WEB OF SCIENCETM
checked on Dec 5, 2016
checked on Apr 29, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.