In this paper, we establish the convergence of the Optimal AdaBoost classifier under mild conditions. We frame AdaBoost as a dynamical system, and provide sufficient conditions for the existence of an invariant measure. Employing tools from ergodic theory, we show that the margin for every example converges. More generally, we prove that the time average of any function of the weights over the ...

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An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also estab...

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 show that with the introduction of a scoring function and the random selection of training data it is possible to create a smaller set of feature vectors. The selection of th...

This paper describes the necessity and adopted methods to detect a human face. Since the data is computed by the computer, many algorithms are developed to detect a face. Some of the key challenges for the process of face detection are discussed. Four general face detection methods that are universally used are elaborated with their capabilities, advantages and disadvantages. A rapid approach t...

AdaBoost is a practical method of real-time face detection, but abides by a crucial problem of overfitting for the big number of features used in a trained classifier due to the weak discriminative abilities of these features. This paper proposes a theoretical approach to construct highly discriminative features, which is named composed features, from Haar-like features. Both of the composed an...

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...

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 ...