Combining Expert Advice Efficiently

Details Combining Expert Advice Efficiently Combining Expert Advice Efficiently

2008-02-14

arxiv.org/abs/0802.2015v2

Computer Science

Learning

50 pages

Scientific paper

We show how models for prediction with expert advice can be defined concisely and clearly using hidden Markov models (HMMs); standard HMM algorithms can then be used to efficiently calculate, among other things, how the expert predictions should be weighted according to the model. We cast many existing models as HMMs and recover the best known running times in each case. We also describe two new models: the switch distribution, which was recently developed to improve Bayesian/Minimum Description Length model selection, and a new generalisation of the fixed share algorithm based on run-length coding. We give loss bounds for all models and shed new light on their relationships.

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