Sparse Bayesian learning (SBL) has attracted substantial interest in recent years for reliable estimation of sparse parameter vectors of dimension much larger than the number of measurements. However, the theory of online sequential estimation of sparsely changing parameter vectors is much less studied....
Yuan, Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning. Sci. China-Technol. Sci. 65, 386–395 (2022) Google Scholar J. Shu, S. Wang, S. Yu, J. Zhang, CFSA-Net: efficient large-scale point cloud semantic segmentation based on...
Dai, J., So, H.C.: Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation. IEEE Trans. Signal Process. 66(3), 744–756 (2017) Article MathSciNet Google Scholar Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump...
either using approaches such aspenalised regressionandshrinkage, or throughBayesian regression. The importance of regularisation has also been recognised in deep learning and further exchange here could be beneficial.
reduced by incorporating model regularisation, either using approaches such as penalised regression and shrinkage, or through Bayesian regression. The importance of regularisation has also been recognised in deep learning and further exchange here could be ...
Multi-bearing remaining useful life collaborative prediction: A deep learning approach J. Manuf. Syst. (2017) RenL. et al. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks J. Manuf. Syst. (2018) WangB. et al. Periodical sparse low-rank matrix estima...
In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning...
Bayesian network structure learning by recursive autonomy identification - Yehezkel, Lerner - 2009 () Citation Context ...hods: as a user default [10, 29, 31, 33], based on limited trial and error experimentation [5], or automatically as the threshold, from a range of candidates, that max...
In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning...
Although variable, people's judgments are broadly consistent with the model and inconsistent with several alternatives, including a pre-trained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmicions from sparse input data....