### The Weighted Majority Algorithm

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[1] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[2] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1988, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[3] Carl H. Smith,et al. Probability and Plurality for Aggregations of Learning Machines , 1987, Inf. Comput..

[4] Alfredo De Santis,et al. Learning probabilistic prediction functions , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[5] Esther Levin,et al. A statistical approach to learning and generalization in layered neural networks , 1989, COLT '89.

[6] Manfred K. Warmuth,et al. The weighted majority algorithm , 1989, 30th Annual Symposium on Foundations of Computer Science.

[7] Manfred K. Warmuth,et al. Learning Nested Differences of Intersection-Closed Concept Classes , 1989, COLT '89.

[8] Nick Littlestone,et al. From on-line to batch learning , 1989, COLT '89.

[9] Leonard Pitt,et al. Probabilistic inductive inference , 1989, JACM.

[10] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[11] Vladimir Vovk,et al. Aggregating strategies , 1990, COLT '90.

[12] N. Littlestone. Mistake bounds and logarithmic linear-threshold learning algorithms , 1990 .

[13] Wolfgang Maass,et al. On-line learning with an oblivious environment and the power of randomization , 1991, COLT '91.

[14] David Haussler,et al. Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise , 1991, COLT '91.

[15] Michael Kearns,et al. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[16] Thomas G. Dietterich. Machine learning , 1996, CSUR.