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:2013:10.1109/TPAMI.2012.208,
author = {Chatzis, S and Demiris, Y},
doi = {10.1109/TPAMI.2012.208},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {1523--1534},
title = {The Infinite-Order Conditional Random Field Model for Sequential Data Modeling},
url = {http://dx.doi.org/10.1109/TPAMI.2012.208},
volume = {6},
year = {2013}
}

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 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 can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF model is experimentally demonstrated.
AU - Chatzis,S
AU - Demiris,Y
DO - 10.1109/TPAMI.2012.208
EP - 1534
PY - 2013///
SN - 0162-8828
SP - 1523
TI - The Infinite-Order Conditional Random Field Model for Sequential Data Modeling
T2 - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR - http://dx.doi.org/10.1109/TPAMI.2012.208
UR - http://hdl.handle.net/10044/1/12614
VL - 6
ER -