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Clustering Based on Gaussian Processes
Neural Computation, 2007In this letter, we develop a gaussian process model for clustering. The variances of predictive values in gaussian processes learned from a training data are shown to comprise an estimate of the support of a probability density function. The constructed variance function is then applied to construct a set of contours that enclose the data points, which
Kim, HC, Lee, J
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IEEE Transactions on Neural Networks, 2011
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 ...
Sotirios P. Chatzis, Yiannis Demiris
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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 ...
Sotirios P. Chatzis, Yiannis Demiris
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Gaussian Processes and Neuronal Modeling
Natural Computing, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elvira Di Nardo +3 more
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On Gaussian Markov processes and Polya processes
Operations Research LetterszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kerry W. Fendick, Ward Whitt
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2004
We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP.
Snelson, E. +2 more
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We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP.
Snelson, E. +2 more
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Gaussian Variables and Gaussian Processes
2016Gaussian random processes play an important role both in theoretical probability and in various applied models. We start by recalling basic facts about Gaussian random variables and Gaussian vectors. We then discuss Gaussian spaces and Gaussian processes, and we establish the fundamental properties concerning independence and conditioning in the ...
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On integrating prior knowledge into Gaussian processes for prognostic health monitoring
Mechanical Systems and Signal Processing, 2022Simon Pfingstl
exaly
Latent map Gaussian processes for mixed variable metamodeling
Computer Methods in Applied Mechanics and Engineering, 2021Nicholas Oune, Ramin Bostanabad
exaly
Gaussian processes for time-series modelling
Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2013Sally Roberts, S Reece, S Aigrain
exaly
Extremes of a certain class of Gaussian processes
Stochastic Processes and Their Applications, 1999J Husler, Vladimir I Piterbarg
exaly