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The AdaBoost algorithm was designed to combine many "weak" hypotheses that perform slightly better than random guessing into a "strong" hypothesis that has very low error. We study the rate at which AdaBoost iteratively converges to the minimum of the "exponential loss". Unlike previous work, our pr ...

One of the most successful and popular learning algorithms is AdaBoost, which is a classification algorithm designed to construct a "strong" classifier from a "weak" learning algorithm. Just after the development of AdaBoost nine years ago, scientists derived margin- based generalization bounds to e ...

We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; howev ...

It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a ...