Combining Expert Advice Efficiently

8 years 6 months ago
Combining Expert Advice Efficiently
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 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 runlength coding. We give loss bounds for all models and shed new light on the relationships between them.
Wouter M. Koolen, Steven de Rooij
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2008
Where COLT
Authors Wouter M. Koolen, Steven de Rooij
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