BibTex format

author = {Soh, H and Demiris, Y},
doi = {10.1109/TNNLS.2014.2316291},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {522--536},
title = {Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes},
url = {},
volume = {26},
year = {2015}

RIS format (EndNote, RefMan)

AB - Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.
AU - Soh,H
AU - Demiris,Y
DO - 10.1109/TNNLS.2014.2316291
EP - 536
PY - 2015///
SN - 2162-2388
SP - 522
TI - Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes
T2 - IEEE Transactions on Neural Networks and Learning Systems
UR -
UR -
UR -
VL - 26
ER -