Computer Vision and Image Processing
Adaboost, or Adaptive Boosting, is a machine learning ensemble technique that combines multiple weak classifiers to create a strong classifier. By focusing on the errors made by previous classifiers and giving them more weight, Adaboost iteratively improves the overall accuracy of the model. This method is particularly effective in supervised learning tasks, where it enhances classification performance and is also applicable in tasks like edge-based segmentation for improving object detection and recognition.
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