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Curtailed Online Boosting

Pelossof, Raphael; Jones, Michael

The purpose of this work is to lower the average number of features that are evaluated by an online algorithm. This is achieved by merging Sequential Analysis and Online Learning. Many online algorithms use the example's margin to decide whether the model should be updated. Usually, the algorithm's model is updated when the margin is smaller than a certain threshold. The evaluation of the margin for each example requires the algorithm to evaluate all the model's features. Sequential Analysis allows us to early stop the computation of the margin when uninformative examples are encountered. It is desirable to save computation on uninformative examples since they will have very little impact on the final model. We show the successful speedup of Online Boosting while maintaining accuracy on a synthetic and the MNIST data sets.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-040-09
Published Here
July 16, 2010
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