There is interest in extending the boosting algorithm (Schapire, 1990) to fit a wide range of regression problems. The threshold-based boosting al- gorithm for regression sued an analogy between classification errors and big errors in regression. We focus on the practical aspects of this algo- rithm and compare it to other attempts to extend boosting to regression. The practical capabilities of this model are demonstrated on the laser data From the Santa Fe times-series competition and the Mackey-Glass time Series, where the results surpass those of standard ensemble average.
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