A probabilistic learning algorithm for robust modeling using neural networks with random weights
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Publication:1749195
DOI10.1016/J.INS.2015.03.039zbMath1387.68196OpenAlexW2051404524MaRDI QIDQ1749195
Dianhui Wang, Hailiang Ye, Feilong Cao
Publication date: 16 May 2018
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2015.03.039
outliersLaplace distributionexpectation maximizationneural networks with random weights (NNRWs)probabilistic robust learning
Related Items (7)
Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression ⋮ An iterative learning algorithm for feedforward neural networks with random weights ⋮ Fuzzy nonlinear regression analysis using a random weight network ⋮ Design of stabilized polynomial-based ensemble fuzzy neural networks based on heterogeneous neurons and synergy of multiple techniques ⋮ Meta fuzzy functions based feed-forward neural networks with a single hidden layer for forecasting ⋮ Robust stochastic configuration networks with kernel density estimation for uncertain data regression ⋮ Relaxed support vector regression
Uses Software
Cites Work
- Weighted least squares support vector machines: robustness and sparse approximation
- Decoding by Linear Programming
- Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse
- The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
- Sparse Approximate Solutions to Linear Systems
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Compressed sensing
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