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...

This paper introduces AdaBoost Dynamic, an extension of AdaBoost.M1 algorithm by Freund and Shapire. In this extension we use different “weak” classifiers in subsequent iterations of the algorithm, instead of AdaBoost’s fixed base classifier. The algorithm is tested with various datasets from UCI database, and results show that the algorithm performs equally well as AdaBoost with the best possi...

Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets. Our empirical study shows that our me...

This paper presents a learning algorithm based on AdaBoost for solving two-class classification problem. The concept of boosting is to combine several weak learners to form a highly accurate strong classifier. AdaBoost is fast and simple because it focuses on finding weak learning algorithms that only need to be better than random, instead of designing an algorithm that learns deliberately over...

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a ca...

AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because its objective is to minimize error on the training set. We demonstrate that overfitting in AdaBoost can be alleviated in a time-efficient manner using a combination of dagging and validation sets. Half of the training set is removed ...

AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because it focuses on misclassified examples, which may be noisy. We demonstrate that overfitting in AdaBoost can be alleviated in a time-efficient manner using a combination of dagging and validation sets. The training set is partitioned in...

Abstract This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm’s optimal solution, and it demonstrates how classifier accuracy can b...

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducin...