Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control ...
Various cognitive and computational models have addressed the use of previous experience to understand a new domain. In particular, research in case-based reasoning has explored t...
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Lecturing a Discrete Event Simulation course implies some challenges for the instructors. These challenges implies taking decisions from the design of the course to the selection ...
Abstract. Bias variance decomposition for classifiers is a useful tool in understanding classifier behavior. Unfortunately, the literature does not provide consistent guidelines on...