We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.

www.cs.tau.ac.il/˜nin Abstract. We study various ensemble methods for hybrid neural networks. The hybrid networks are composed of radial and projection units and are trained using a deterministic algorithm that completely defines the parameters of the network for a given data set. Thus, there is no random selection of the initial (and final) parameters as in other training algorithms. Network independent is achieved by using bootstrap and boosting methods as well as random input sub-space sampling. The fusion methods are evaluated on several classification benchmark data-sets. A novel MDL based fusion method appears to reduce the variance of the classification scheme and sometimes be superior in its overall performance. 1