In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagran...
Jean-Philippe Tarel, Pierre Charbonnier, Sio-Song ...
—This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm. Distributed implementations run under Hadoop on facilit...
Sebastien Bratieres, Jurgen Van Gael, Andreas Vlac...
Workflow mining is the task of automatically producing a workflow model from a set of event logs recording sequences of workflow events; each sequence corresponds to a use case or ...
Javier Esparza, Martin Leucker, Maximilian Schlund
Abstract. Ant algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of "logis...
Robots operating in a workspace can localize themselves by querying nodes of a sensor-network deployed in the same workspace. This paper addresses the problem of computing the min...