Enlarging the Margins in Perceptron Decision Trees

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we provide an upper bound on the generalization error which depends both on the size of the tree and on the margin of the decision nodes. So enlarging the margin in perceptron decision trees will reduce the upper bound on generalization error. Based on this analysis, we introduce three new algorithms, which can induce large margin perceptron decision trees. To assess the effect of the large margin bias, OC1 (Journal of Artificial Intelligence Research, 1994, 2, 1

• Type: Journal
• Year: 2000
• Where: ML
• Authors: Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor, Donghui Wu
• Keywords: Generalization Error, Machine Learning, ML 2000, Perceptron Decision, Upper Bound