Data-Dependent Structural Risk Minimization for Perceptron Decision Trees
Perceptron Decision Trees (also known as Linear Machine DTs, etc.) are analysed in order that data-dependent Structural Risk Minimisation can be applied. Data-dependent analysis is performed which indicates that choosing the maximal margin hyperplanes at the decision nodes will improve the generalization. The analysis uses a novel technique to bound the generalization error in terms of the margins at individual nodes. Experiments performed on real data sets confirm the validity of the approach.