Boosting Algorithms as Gradient Descent

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Boosting Algorithms as Gradient Descent (2000)

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by Llew Mason , Jonathan Baxter , Peter Bartlett , Marcus Frean

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156 - 1 self

BibTeX

@MISC{Mason00boostingalgorithms,
    author = {Llew Mason and Jonathan Baxter and Peter Bartlett and Marcus Frean},
    title = {Boosting Algorithms as Gradient Descent},
    year = {2000}
}

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Abstract

Much recent attention, both experimental and theoretical, has been focussed on classification algorithms which produce voted combinations of classifiers. Recent theoretical work has shown that the impressive generalization performance of algorithms like AdaBoost can be attributed to the classifier having large margins on the training data. We present an abstract algorithm for finding linear combinations of functions that minimize arbitrary cost functionals (i.e functionals that do not necessarily depend on the margin). Many existing voting methods can be shown to be special cases of this abstract algorithm. Then, following previous theoretical results bounding the generalization performance of convex combinations of classifiers in terms of general cost functions of the margin, we present a new algorithm (DOOM II) for performing a gradient descent optimization of such cost functions. Experiments on

Keyphrases

gradient descent abstract algorithm arbitrary cost functionals classification algorithm new algorithm training data generalization performance voted combination previous theoretical result linear combination recent theoretical work impressive generalization performance large margin much recent attention general cost function gradient descent optimization doom ii convex combination cost function special case

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