Optimally Combining Classifiers Using Unlabeled Data

author: Akshay Balsubramani, Department of Computer Science and Engineering, UC San Diego
published: Aug. 20, 2015,   recorded: July 2015,   views: 1683


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We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solution of this game identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.

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