The Asymptotic Performance of AdaBoost

Google Tech Talks
May 24, 2007

ABSTRACT

Many popular classification algorithms, including AdaBoost and the support vector machine, minimize a cost function that can be viewed as a convex surrogate of the 0-1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. In this talk, we consider the universal consistency of such methods: does the risk, or expectation of the 0-1 loss, approach its optimal value, no matter what i.i.d. process generates. Credits: Speaker:Peter Bartlett

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Howto & Style

Tags:

• google
• howto
• asymptotic
• performance
• adaboost