Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Abstract. Haplotype Inference (HI) is a computational challenge of crucial importance in a range of genetic studies, such as functional genomics, pharmacogenetics and population ge...
Two central criteria for data quality are consistency and accuracy. Inconsistencies and errors in a database often emerge as violations of integrity constraints. Given a dirty dat...
We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature red...
Bernd Heisele, Thomas Serre, Sayan Mukherjee, Toma...