Results 221 to 230 of about 1,248,103 (272)
Hybrid Gaussian process regression with temporal feature extraction for partially interpretable remaining useful life interval prediction in Aeroengine prognostics. [PDF]
Sci RepNiu T, Xu Z, Luo H, Zhou Z.
europepmc +1 more source
Planning under uncertainty for safe robot exploration using Gaussian process prediction. [PDF]
Auton RobotsStephens A +7 more
europepmc +1 more source
Predicting hydrogen atom transfer energy barriers using Gaussian process regression.
Digit DiscovUlanov E +4 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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 ...
Demiris, Yiannis, Chatzis, Sotirios P.
openaire +3 more sources
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 ...
Demiris, Yiannis, Chatzis, Sotirios P.
openaire +3 more sources
Acta Mathematicae Applicatae Sinica, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
2005
Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
openaire +1 more source
Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
openaire +1 more source
1999
Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
openaire +1 more source
Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
openaire +1 more source
Gaussian Process Regression for Materials and Molecules
Chemical Reviews, 2021Volker L Deringer +2 more
exaly