Margin theory provides one of the most popular explanations to the success of AdaBoost, where the central point lies in the recognition that margin is the key for characterizing the performance of AdaBoost. This theory has been very influential, e.g., it has been used to argue that AdaBoost usually does not overfit since it tends to enlarge the margin even after the training error reaches zero....

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AdaBoost, a recent v ersion of Boosting is known to improve the performance of decision trees in many classiication problems, but in some cases it does not do as well as expected. There are also a few reports of its application to more complex classiiers such as neural networks. In this paper we decompose and modify this algorithm for use with RBF NNs, our methodology being based on the techniq...

One of the major developments in machine learning in the past decade is the Ensemble method, which finds a highly accurate classifier by combining many moderately accurate component classifiers. In this paper, we propose a classifier of integrated neuro-fuzzy system with Adaboost algorithm. It is called Hybrid-neuro-fuzzy system and Adaboost-classifier classifier. Herein, Adaboost creates a col...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

This article will give a general overview of boosting and in particular AdaBoost. AdaBoost is the most popular boosting algorithm. It has been shown to have very interesting properties such as low generalization error as well as an exponentially decreasing bound on the training error. The article will also give a short introduction to learning algorithms.

0 20 40 60 80 100 Margin Cumulative % Sonar Cumulative training margin distributions for AdaBoost versus our \Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacriices signiicant training error for improved test error (horizontal marks on margin= 0 line).

This paper proposes a novel boosting algorithm called VadaBoost which is motivated by recent empirical Bernstein bounds. VadaBoost iteratively minimizes a cost function that balances the sample mean and the sample variance of the exponential loss. Each step of the proposed algorithm minimizes the cost efficiently by providing weighted data to a weak learner rather than requiring a brute force e...