In this paper we study neural network overfitting on synthetically generated and real remote sensing data. The effect of overfitting is shown by: 1) visualising the shape of the decision boundaries in feature space during the learning process, and 2) by plotting the classification accuracy of independent test sets versus the number of training cycles. A solution to the overfitting problem is pr...

We compare firm-optimizing and institutional models of labor contracts to investigate how certain types of pension plans affect training. Unlike previous studies, we consider an expanded voice model of training and pension coverage in which worker and union preferences feed back upon firm decisions and we test for this bi-directional causality between pensions and training. The data consist of ...

We propose a new method for training iterative collective classifiers for labeling nodes in network data. The iterative classification algorithm (ICA) is a canonical method for incorporating relational information into the classification process. Yet, existing methods for training ICA models rely on computing relational features using the true labels of the nodes. This method introduces a bias ...