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ICML
2010
IEEE

Random Spanning Trees and the Prediction of Weighted Graphs

9 years 3 months ago
Random Spanning Trees and the Prediction of Weighted Graphs
We show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the graph. The cutsize is induced by the unknown adversarial labeling of the graph nodes. In deriving our characterization, we obtain a simple randomized algorithm achieving the optimal mistake bound on any weighted graph. Our algorithm draws a random spanning tree of the original graph and then predicts the nodes of this tree in constant amortized time and linear space. Experiments on real-world datasets show that our method compares well to both global (Perceptron) and local (label-propagation) methods, while being much faster.
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella
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