In order to solve the overfitting of sample weights and the low detection rate in training process of the traditional AdaBoost algorithm, an improved AdaBoost algorithm based on Haar-like features and LBP features is proposed. This method improves weight updating rule and weights normalization rule of the traditional AdaBoost algorithm. Then combining this method with the AdaBoost algorithm bas...

AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine SVM , neural networks NN , naı̈ve Bayes, and k-nearest neighbor kNN . This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple tw...

Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As a result, a multitude of asymmetric versions of AdaBoost have been proposed, mainly as heuristic modifications to the original algorithm. In this paper we chal...

AdaBoost is an iterative algorithm to constructclassifier ensembles. It quickly achieves high accuracy by focusingon objects that are difficult to classify. Because of this, AdaBoosttends to overfit when subjected to noisy datasets. We observethat this can be partially prevented with the use of validationsets, taken from the same noisy training set. But using less thanth...

We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answers “+1” if at least r of k relevant variables are positive, and answers “−1” otherwise. Given m examples of a r-of-k function and literals as base hypotheses, popular boosting algorithms (e.g., AdaBoost [FS97]) construct a consistent final hyp...

We explore the relation between the Adaboost weight update procedure and Kelly’s theory of betting. Specifically, we show that an intuitive optimal betting strategy can easily be interpreted as the solution of the dual of the classical formulation of the Adaboost minimisation problem. This sheds new light over a substantial simplification of Adaboost that had so far only been considered a mere ...

Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was successfully applied to solve the problems of object detection, text analysis, data mining and etc. The most and widely used boosting algorithm is AdaBoost and its later more effective variations Gentle and Real AdaBoost. In this article we propose a new boosting algorithm, whi...

There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithm...