Building projectable classifiers of arbitrary complexity

Conventional methods for classifier design often suffer from having two conflicting goals-to develop arbitrarily complex decision boundaries to suit a given problem, and at the same time to constrain the complexity of those boundaries to avoid overfitting given training data. A recent analysis reveals that the conflict is resolvable by building classifiers based on projectable elements, which are weak discriminators that perform equally well for both training and testing data. Based on this analysis, we present a method that constructs a classifier up to arbitrary complexity while presenting generalization accuracy.