The Weighted Majority Algorithm

The construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes is studied. It is assumed that the learner has reason to believe that one of some pool of known algorithms will perform well but does not know which one. A simple and effective method, based on weighted voting, is introduced for constructing a compound algorithm in such a circumstance. It is called the weighted majority algorithm and is shown to be robust with respect to errors in the data. Various versions of the weighted majority algorithm are discussed, and error bounds for them that are closely related to the error bounds of the best algorithms of the pool are proved. >

Authors

• Nick Littlestone
• Manfred K. Warmuth

Keywords

• Prediction algorithms
• Protocols
• Laboratories
• Voting
• Algorithm design and analysis
• Loss Of Generality
• Time Constant
• Total Weight
• Part Of Research
• Sum Of Weights
• Subintervals
• Sequence Segments
• Threshold Function
• Points In Domain
• Finite Sum
• Algorithm In Section
• Boolean Function
• Sabbatical
• Number Of Mistakes
• Number Of Anomalies
• Integer In The Range