Data acquisition of chemical engineering processes is expensive and the collected data are always contaminated with inevitable measurement errors. E cient algorithms are required to lter out the noise and capture the true underlying trend hidden in the training data sets. Regularization networks, which are the exact solution of multivariate linear regularization problems, provide an appropriate...

The artificial neural network (ANN) test of Lee et al. (Journal of Econometrics 56, 269–290, 1993) uses the ability of the ANN activation functions in the hidden layer to detect neglected functional misspecification. As the estimation of the ANN model is often quite difficult, LWG suggested activate the ANN hidden units based on randomly drawn activation parameters. To be robust to the random a...

In view of its ongoing importance for a variety of practical applications, feature selection via `1-regularization methods like the lasso has been subject to extensive theoretical as well empirical investigations. Despite its popularity, mere `1-regularization has been criticized for being inadequate or ineffective, notably in situations in which additional structural knowledge about the predic...