A Real Generalization of Discrete AdaBoost

Scaling discrete AdaBoost to handle real-valued weak hypotheses has often been done under the auspices of convex optimization, but little is generally known from the original boosting model standpoint. We introduce a novel generalization of discrete AdaBoost which departs from this mainstream of algorithms. From the theoretical standpoint, it formally displays the original boosting property; furthermore, it brings interesting computational and numerical improvements that make it significantly easier to handle "as is". Conceptually speaking, it provides a new and appealing scaling to R of some well known facts about discrete (ada)boosting. Perhaps the most popular is an iterative weight modification mechanism, according to which examples have their weights decreased iff they receive the right class by the current discrete weak hypothesis. Our generalization to real values makes that decreasing weights affect only the examples on which the hypothesis' margin exceeds its av...

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Richard Nock, Frank Nielsen
• Keywords: Artificial Intelligence, Discrete Adaboost, ECAI 2006, Original Boosting, Real-valued Weak Hypotheses