The SAMME.C2 algorithm addresses severely imbalanced multi-class classification problems by utilizing boosting techniques such as AdaBoost and cost-sensitive learning. Through numerical experiments and performance statistics, the algorithm shows the trade-off between accurately classifying minority

0 views • 5 slides

Explore the discussion around the necessity of cost-sensitive boosting algorithms as a unified approach in machine learning. Discover the boosting approach, Adaboost algorithm, theoretical history, and comparison with traditional learning algorithms. Dive into the process of turning weak learners in

0 views • 33 slides

Object detection techniques employ cascades, Haar-like features, integral images, feature selection with Adaboost, and statistical modeling for efficient and accurate detection. The Viola-Jones algorithm, Dalal-Triggs method, deformable models, and deep learning approaches are prominent in this fiel

0 views • 21 slides

Face detection using AdaBoost algorithm involves training a sequence of weak classifiers to form a strong final classifier. The process includes weighted data sampling, modifying AdaBoost for Viola-Jones face detector features, and more. Face detection and recognition technology is advancing rapidly

0 views • 49 slides