We propose new and improved instantiations of lossy trapdoor functions (Peikert and Waters, STOC ’08), and correlation-secure trapdoor functions (Rosen and Segev, TCC ’09). Ou...
David Mandell Freeman, Oded Goldreich, Eike Kiltz,...
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...
Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural g...
Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
This paper proposes a novel wideband modeling technique for high-performance RF passives and linear(ized) analog circuits. The new method is based on a recently proposed sdomain h...
A novel center-based clustering algorithm is proposed in this paper. We first formulate clustering as an NP-hard linear integer program and we then use linear programming and the ...