Christoph Baumgarten

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A model for optimal information retrieval over a distributed document collection is described and experimentally evaluated. The fusion of retrieval results corresponding to document subcollections is performed according to the Probability Ranking Principle. Part of the model is a selection criterion for eeectively limiting the ranking process to a subset of(More)
To my parents Acknowledgements I would like to thank all those who gave me support for my work. My supervisor Prof. Dr. Klaus Meyer-Wegener deserves special thanks. Without his help, his encouragements, and the freedom he gave me to realize own ideas, this work would not have been possible. I am deeply indebted to Prof. Dr. Peter Schh auble, who gave me the(More)
In this paper a new model and architecture for information retrieval in a widely distributed hetero-genous multimedia document collection is described. The model generalizes existing probabilistic models for non-distributed information retrieval. The architecture is a conceptual realization of this model. It is hierarchically built in order to provide(More)
This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment. The model assumes the collection of documents offered by the environment to be partitioned into subcollec-tions. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be(More)
We consider a class of optimal stopping problems for a regular one-dimensional diffusion whose payoff depends on a linear parameter. As shown in [Bank and Föllmer(2003)] problems of this type may allow for a universal stopping signal that characterizes optimal stopping times for any given parameter via a level-crossing principle of some auxiliary process.(More)