Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7237
Title: The echo state conditional random field model for sequential data modeling
Authors: Chatzis, Sotirios P. 
Demiris, Yiannis 
Keywords: Computer science;Artificial intelligence;Expert systems (Computer science);Computational linguistics;Regression analysis
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: Sep-2012
Publisher: Elsevier
Source: Expert systems with applications, 2012, vol. 39, no. 11, pp. 10303–10309
Journal: Expert systems with applications 
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
Type: Article
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