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December 20, Monday
15:00 – 16:30

Boosting is a general method in machine learning to improve the prediction accuracy of weak learners. A classic boosting algorithm that deals with the case of deciding between two possible classes is the Adaboost algorithm of Freund and Shpire (JCSS 1997). We propose a new approach to generalize AdaBoost to the multi-class setting. The basic idea is to map labels and confidence-based classifiers to a normed vector space, and to measure performance by distances in this space. The result is a meta-algorithm whose concrete implementations can address various scenarios, from the standard case where each example is assigned to a single class, to more complex settings where each example may belong to a set of classes, and where there is a structure on the label-space which can be captured by distances in a normed space. Joint work with Michal Rosen-Zvi.