Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensi...
Abstract— This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric ma...
Paul E. Rybski, Franziska Zacharias, Jean-Fran&cce...
— This paper presents an architecture and algorithms for optimizing the performance of web services. For a given service, session-based admission control is combined with stage-w...
: Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm fo...
Jan Grau, Jens Keilwagen, Alexander E. Kel, Ivo Gr...
Abstract. We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which meas...