In this section
@inproceedings{Soh:2012:10.1109/IJCNN.2012.6252504,
author = {Soh, H and Demiris, Y},
doi = {10.1109/IJCNN.2012.6252504},
pages = {1--8},
publisher = {IEEE},
title = {Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process},
url = {http://dx.doi.org/10.1109/IJCNN.2012.6252504},
year = {2012}
}
TY - CPAPER
AB - In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing predictive distributions (instead of point predictions). Our method can be seen as a combination of the echo state network with a sparse approximation of Gaussian processes (GPs). Extensive experiments on the one-step prediction task on well-known benchmark problems show that OESGP produced statistically superior results to current online ESNs and state-of-the-art regression methods. In addition, we characterise the benefits (and drawbacks) associated with the considered online methods, specifically with regards to the trade-off between computational cost and accuracy. For a high-dimensional action recognition task, we demonstrate that OESGP produces high accuracies comparable to a recently published graphical model, while being fast enough for real-time interactive scenarios.
AU - Soh,H
AU - Demiris,Y
DO - 10.1109/IJCNN.2012.6252504
EP - 8
PB - IEEE
PY - 2012///
SN - 2161-4393
SP - 1
TI - Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process
UR - http://dx.doi.org/10.1109/IJCNN.2012.6252504
UR - http://hdl.handle.net/10044/1/12655
ER -
