The Decision List Machine

We introduce a new learning algorithm for decision lists to allow features that are constructed from the data and to allow a trade- ofi between accuracy and complexity. We bound its generalization error in terms of the number of errors and the size of the classifler it flnds on the training data. We also compare its performance on some natural data sets with the set covering machine and the support vector machine.

Authors

• Marina Sokolova Stan Szpakowicz
• Mario Marchand
• Nathalie Japkowicz
• John Shawe-Taylor University College London

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