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...

”Boosting” is a general method for improving the performance of almost any learning algorithm. A recently proposed and very promising boosting algorithm is AdaBoost [7]. In this paper we investigate if AdaBoost can be used to improve a hybrid HMM/ neural network continuous speech recognizer. Boosting significantly improves the word error rate from 6.3% to 5.3% on a test set of the OGI Numbers95...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extension...