This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform var...
Marta Avalos, Yves Grandvalet, Christophe Ambroise
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...
We consider the task of creating a 3-d model of a large novel environment, given only a small number of images of the scene. This is a difficult problem, because if the images are...
The Gaussian mixture model (GMM) can approximate arbitrary probability distributions, which makes it a powerful tool for feature representation and classification. However, it su...