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Abstract

This talk will survey recent work on understanding Boosting in the context of maximizing the margin of separation. Starting with a brief introduction into Boosting in general and AdaBoost in particular, I will illustrate the connection to von Neumann's Minimax theorem and discuss AdaBoost's strategy to achieve a large margin. This will be followed by a presentation of algorithms which provably maximize the margin, are considerably quicker in maximizing the margin in practice and implement the soft- margin idea to improve the robustness against noise. In the second part I will discuss how these techniques relate to other convex optimization techniques and how they are connected to Support Vector Machines. Finally, I will talk about the effects of the different key ingredients of Boosting and lessons learned from the application of such algorithms to real world problems.