AdaBoost has proved to be an effective method to improve the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost might suffer from the overfitting problem, especially for noisy data. In addition, most current work on boosting assumes that the combination weights are fixed constants and therefore does not take particular input pa...

We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answer “+1” if at least r of k relevant variables are positive, and answer “−1” otherwise. Given m examples of a r-of-k function and literals as base hypotheses, popular boosting algorithms (e.g., AdaBoost) construct a consistent final hypothesis b...

The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, challenging to apply the AdaBoost directly pulmonary nodule detection of labeled unlabeled lung CT images since there are still some drawbacks method. Therefore, solve data problem, semi-supervised using an improved sparrow search alg...

Recently ensemble methods like AdaBoost were successfully applied to character recognition tasks, seemingly defying the problems of overrtting. This paper shows that although AdaBoost rarely overrts in the low noise regime it clearly does so for higher noise levels. Central for understanding this fact is the margin distribution and we nd that AdaBoost achieves { doing gradient descent in an err...

The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we consider the stopping strategy to be used in AdaBoost to achieve universal consistency. We show that provided AdaBoost is stopped after n iterations—for sample size n and ν < 1—the sequence of risks of the classifiers it produces approaches the Bayes risk if Bayes risk L∗ > 0.

The significance of the study of the theoretical and practical properties of AdaBoost is unquestionable, given its simplicity, wide practical use, and effectiveness on real-world datasets. Here we present a few open problems regarding the behavior of “Optimal AdaBoost,” a term coined by Rudin, Daubechies, and Schapire in 2004 to label the simple version of the standard AdaBoost algorithm in whi...

Machine Learning tools are increasingly being applied to analyze data from microarray experiments. These include ensemble methods where weighted votes of constructed base classifiers are used to classify data. We compare the performance of AdaBoost, bagging and BagBoost on gene expression data from the yeast cell cycle. AdaBoost was found to be more effective for the data than bagging. BagBoost...

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical...

The success of mobile robots relies on the ability to extract from the environment additional information beyond simple spatial relations. In particular, mobile robots need to have semantic information about the entities in the environment such as the type or the name of places or objects. This work addresses the problem of classifying places (room, corridor or doorway) using mobile robots equi...