Context Tree Weighting is a powerful probabilistic sequence prediction technique that efficiently performs Bayesian model averaging over the class of all prediction suffix trees of bounded depth. In this paper we show how to generalize this technique to the class of K-skip prediction suffix trees. Contrary to regular prediction suffix trees,K-skip prediction suffix trees are permitted to ignore...

One can adapt the context tree weighting method in such a way, that it will find the minimum description length model (MDL-model) that corresponds to the data. In this paper this new algorithm, the context tree maximizing algorithm, and a few modifications of the algorithm will be studied, in particular, its performance if we apply it for data compression.

Weare pleased to present this special issue ofDiscrete AppliedMathematics, which is devoted to the 12th Cologne-Twente Workshop on Graphs and Combinatorial Optimization (CTW 2013), held at the University of Twente, Enschede, The Netherlands, between May 21 and May 23, 2013. More than 80 scientists from all over the world attended the workshop, and we take this opportunity to thank all CTW 2013 ...

The height assignment of atmospheric motion vectors (AMV) is still an open issue. From the numerous height assignment techniques, each retrieval method has its own characteristics. The presented stereophotogrammetric method has the advantage that it is only dependent on the geometry and therefore offers a completely independent height retrieval compared to the other methods. As polar-orbiting i...

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