AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present ...

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...

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...

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...

In this paper we apply multi-armed bandits (MABs) to improve the computational complexity of AdaBoost. AdaBoost constructs a strong classifier in a stepwise fashion by selecting simple base classifiers and using their weighted “vote” to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs where each arm ...

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.

”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...

The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11–21] show that AdaBoost with strong component...