2019
Bonilla-Escribano, P; Ramírez, David; Sedano-Capdevila, Alba; Campaña-Montes, Juan Jose; Baca-García, Enrique; Courtet, Philippe; Artés-Rodríguez, Antonio
Assessment of e-social activity in psychiatric patients Artículo de revista
En: IEEE J. Biomedical and Health Informatics, vol. 23, no 6, pp. 2247-2256, 2019, ISSN: 2168-2194.
Enlaces | BibTeX | Etiquetas: E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes
@article{Bonilla-Escribano2019,
title = {Assessment of e-social activity in psychiatric patients},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Alba Sedano-Capdevila and Juan Jose Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2019.2918687},
issn = {2168-2194},
year = {2019},
date = {2019-11-01},
journal = {IEEE J. Biomedical and Health Informatics},
volume = {23},
number = {6},
pages = {2247-2256},
keywords = {E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes},
pubstate = {published},
tppubtype = {article}
}
2015
Nazabal, Alfredo; Artés-Rodríguez, Antonio
Discriminative spectral learning of hidden markov models for human activity recognition Proceedings Article
En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1966–1970, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training
@inproceedings{Nazabal2015,
title = {Discriminative spectral learning of hidden markov models for human activity recognition},
author = {Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178314},
doi = {10.1109/ICASSP.2015.7178314},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1966--1970},
publisher = {IEEE},
address = {Brisbane},
abstract = {Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.},
keywords = {Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
2009
Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems Artículo de revista
En: IEEE Transactions on Communications, vol. 57, no 8, pp. 2339–2347, 2009, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines
@article{Murillo-Fuentes2009,
title = {Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems},
author = {Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5201027},
issn = {0090-6778},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Communications},
volume = {57},
number = {8},
pages = {2339--2347},
abstract = {In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.},
keywords = {analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines},
pubstate = {published},
tppubtype = {article}
}