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15

Voted
COLT

2010

Springer

2010

Springer

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.

Related Content

Added |
10 Feb 2011 |

Updated |
10 Feb 2011 |

Type |
Journal |

Year |
2010 |

Where |
COLT |

Authors |
Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen |

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