We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class ...
—Researchers choosing to share wireless-network traces with colleagues must first anonymize sensitive information, trading off the removal of information in the interest of iden...
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
Background: The post-genomic era is characterised by a torrent of biological information flooding the public databases. As a direct consequence, similarity searches starting with ...
Anne Friedrich, Raymond Ripp, Nicolas Garnier, Emm...
Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods ar...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
The known sampling methods can roughly be grouped into regular and irregular sampling. While regular sampling can be realized efficiently in graphics hardware, it is prone to inte...
Sampling is a core process for a variety of graphics applications. Among existing sampling methods, blue noise sampling remains popular thanks to its spatial uniformity and absenc...
Data Stream Management Systems are useful when large volumes of data need to be processed in real time. Examples include monitoring network traffic, monitoring financial transacti...
Theodore Johnson, S. Muthukrishnan, Vladislav Shka...
1 — Sampling is increasingly utilized by passive measurement systems to save the resources consumption. However, the widely adopted static linear sampling selects packets with th...