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The fields of machine learning and mathematical optimization increasinglyintertwined. The special topic on supervised learning and convex optimizationexamines this interplay. The training part of most supervised learningalgorithms can usually be reduced to an optimization problem that minimizes aloss between model predictions and training data. While most optimizationtechniques focus on accuracy and speed of convergence, the qualities of goodoptimization algorithm from the machine learning perspective can be quitedifferent since machine learning is more than fitting the data. Betteroptimization algorithms that minimize the training loss can possibly give verypoor generalization performance. In this paper, we examine a particular kind ofmachine learning algorithm, boosting, whose training process can be viewed asfunctional coordinate descent on the exponential loss. We study the relationbetween optimization techniques and machine learning by implementing a newboosting algorithm. DABoost, based on dual-averaging scheme and study itsgeneralization performance. We show that DABoost, although slower in reducingthe training error, in general enjoys a better generalization error thanAdaBoost.

A new boosting algorithm based on dual averaging scheme