We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels fo...
Andreas Argyriou, Raphael Hauser, Charles A. Micch...
A novel evolutionary approach for the bin packing problem (BPP) is presented. A simple steady-state genetic algorithm is developed that produces results comparable to other approa...
Learning Bayesian networks from data is an N-P hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational comple...
ions of ODE models (MAPLE, GNA). On the algorithmic side (Sec. 3.2), it supports two main streams in high-performance model checking: reachability analysis based on BDDs (symbolic)...
– Reducing power consumption through high-level synthesis has attracted a growing interest from researchers due to its large potential for power reduction. In this work we study ...