- Hedging structured concepts
- 23rd Annual Conference on Learning Theory (COLT 2010), Haifa, Israel
- Book/source title
- Proceedings of the 23rd Annual Conference on Learning Theory (COLT 2010)
- Pages (from-to)
- Madison, WI: Omnipress
- Document type
- Conference contribution
- Interfacultary Research Institutes
- Institute for Logic, Language and Computation (ILLC)
We develop an online algorithm called Component Hedge for learning structured concept classes when the loss of a structured concept sums over its components. Example classes include paths through a graph (composed of edges) and partial permutations (composed of assignments). The algorithm maintains a parameter vector with one non-negative weight per component, which always lies in the convex hull of the structured concept class. The algorithm predicts by decomposing the current parameter vector into a convex combination of concepts and choosing one of those concepts at random. The parameters are updated by first performing a multiplicative update and then projecting back into the convex hull. We show that Component Hedge has optimal regret bounds for a large variety of structured concept classes.
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