PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast and optimal prediction of a labeled tree
Nicolò Cesa-Bianchi, Claudio Gentile and Fabio Vitale
In: Colt 2010, 18-21 June 2009, Montreal, Canada.


We characterize, up to constant factors, the number of mistakes necessary and sufficient for sequentially predicting a given tree with binary labeled nodes. We provide an efficient algorithm achieving this number of mistakes on any tree. Tree prediction algorithms can solve the general graph prediction problem by representing the graph via one of its spanning trees. In order to cope with adversarial assignments of labels over a general graph, we advocate the use of random spanning trees, which have the additional advantage of retaining relevant spectral information of the original graph.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6538
Deposited By:Claudio Gentile
Deposited On:08 March 2010