AdaBoost
AdaBoost (Adaptive Boosting) is a machine learning algorithm that combines multiple weak classifiers to create a strong classifier. In AdaBoost, each weak classifier is trained on a subset of the data, and the algorithm assigns higher weights to misclassified samples in each iteration. The final classifier is a weighted sum of the weak classifiers, where the weights are determined by their accuracy. AdaBoost is particularly useful for classification problems with high-dimensional data and noisy data. It has been successfully applied in various fields, such as computer vision, speech recognition, and bioinformatics.
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