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Neural Networks and Computational Learning Theory

 

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NeuroCOLT Technical Report NC-TR-98-003


Data-Dependent Structural Risk Minimisation for Perceptron Decision Trees


John Shawe-Taylor
RHUL
UK

Nello Cristianini
Bristol
UK

Received: 22-APR-1998

Keywords: perceptron, decision tree, maximal margin


Abstract
Perceptron Decision Trees (also known as Linear Machine DTs, etc.) are analysed in order that data-dependent Structural Risk Minimization 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.

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