Lecture 7 : Full feedback and adversarial rewards ( part II )
نویسنده
چکیده
Previously, we introduced the best-expert problem, and we proved a O(ln K) mistake bound for the majority vote algorithm when a perfect expert exists, i.e., there is an expert that never makes mistakes. Now let us turn to the more realistic case where there is no perfect expert among the committee. We extend the majority vote algorithm with a confidence weight. At each round, we maintain a weight w i for each expert i, and we choose the prediction that has the highest total weights. After observing the feedback, we decay the weights of incorrect experts with a factor of (1 −). This algorithm is called Weighted Majority Algorithm (WMA).
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Lecture 7 : Full feedback and adversarial rewards ( part I )
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