Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chatzis:2012:10.1016/j.eswa.2012.02.193,
author = {Chatzis, SP and Demiris, Y},
doi = {10.1016/j.eswa.2012.02.193},
journal = {Expert Systems With Applications},
pages = {10303--10309},
title = {The echo state conditional random field model for sequential data modeling},
url = {http://dx.doi.org/10.1016/j.eswa.2012.02.193},
volume = {39},
year = {2012}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Chatzis,SP
AU - Demiris,Y
DO - 10.1016/j.eswa.2012.02.193
EP - 10309
PY - 2012///
SN - 0957-4174
SP - 10303
TI - The echo state conditional random field model for sequential data modeling
T2 - Expert Systems With Applications
UR - http://dx.doi.org/10.1016/j.eswa.2012.02.193
VL - 39
ER -