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AdaBoost Explanation Example1 by saiv80089 is a document available to read on EtoBox.
AdaBoost is an ensemble learning method that combines multiple weak learners to create a strong classifier, focusing on misclassified samples in each iteration. The algorithm involves initializing weights, training weak learners, computing errors, and updating weights to emphasize harder examples. The final classifier aggregates the predictions of all weak learners using a weighted majority vote based on their accuracy, allowing for improved performance over individual models.
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