A practical Bayesian framework for backpropagation networks
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Asymptotic efficiency of the two-stage estimation method for copula-based models
Journal of Multivariate Analysis
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Neural Processing Letters
Adaptive mixtures of local experts
Neural Computation
The coefficient of intrinsic dependence (feature selection using el CID)
Pattern Recognition
From Archimedean to Liouville copulas
Journal of Multivariate Analysis
Efficient template-based path imitation by invariant feature mapping
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Financial volatility trading using recurrent neural networks
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
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Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency.