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Sparse multi-output radial basis function network construction using combined locally regularised orthogonal least square and D-Optimality experimental design

open access: yes, 2003
A new construction algorithm for multi-output radial basis function (RBF) network modelling is introduce by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design.
Hong, X., Harris, C.J., Chen, S.
core  

Partial least-squares regression with unlabeled data

open access: yes
It is well known that the prediction errors from principal component regression (PCR) and partial least-squares regression (PLSR) can be reduced by using both labeled and unlabeled data for stabilizing the latent subspaces in the calibration step.
Bonvin, Dominique   +3 more
core  

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